Data Science Groups at utilities can use this information in their work.
Risk Definition Visualization - White paper providing a better understanding of risk and describe approaches for evaluating and visualizing risk to support more effective power delivery asset management.
Transmission Asset Management Guidebook - This report presents the results of efforts to develop and update guides for helping power delivery organizations adapt and implement best practice asset management. Although proven and in wide use in the power generation sector, effective asset management processes have not been as widely applied in power delivery due to a host of challenges.
1 - Transmission Asset Management Guidebook
Abstract
This report presents the results of efforts to develop and update guides for helping power delivery organizations adapt and implement best practice asset management. Although proven and in wide use in the power generation sector, effective asset management processes have not been as widely applied in power delivery due to a host of challenges.
To help address these challenges, EPRI initiated a series of efforts to provide information and guidance to assist power delivery organizations in understanding and applying asset management principles. These efforts first resulted in Guidelines for Power Delivery Asset Management: A Business Model for Program Implementations, EPRI, Palo Alto, CA: 2004. 1008550. The following year, the work was enhanced with the publication of Guidelines for Power Delivery Asset Management, EPRI, Palo Alto, CA: 2005, 1010728. In subsequent years, EPRI focused on addressing the needs for analytical tools and equipment performance databases identified in these guidelines.
With the successful development and acceptance of a number of EPRI analytical tools and databases and the increased appreciation of asset management principles for guiding power delivery organizations, it was deemed appropriate in 2021 to revisit the subject, and the result was Power Delivery Asset Management: 2021 Update, EPRI, Palo Alto, CA: 2021, 3002021198.That update included two utility asset management implementation examples. In accordance with EPRI’s continuing efforts to provide members the most useful and up-to-date Asset Management (AM) resources and to make certain that they fully address current utility needs, this 2024 report documents the results of work to produce the next version of the guide.
This guide presents asset management theory and principles and the application of asset management to power delivery. Also included are discussions of the integration of asset management and equipment maintenance, performance measures and risk. A chapter on implementing an asset management program and two example utility implementations completes the report.
Building on the existing guidelines, using this report as a platform and working with members, EPRI will continue to review and update AM application guidance as needs are identified.
Keywords
Power delivery
Asset management
Transmission
Substations
1.1 - Chapter 1 - Introduction
This report presents the results of efforts to develop an updated guide for helping power delivery organizations adapt and implement best practice asset management. Unlike other asset management guides available, the material presented here was specifically developed for application to the power delivery industry.
Background
Although proven and in wide use in the power generation sector,
effective asset management processes have not been applied as widely in power delivery due to a host of challenges. These challenges include:
Wide diversity in power delivery organizations sizes and structures.
Many possible categories and dimensions to the potential values
created by the asset management process, including financial
returns, system reliability, public and worker safety, and rate
stability.
Various stakeholders, including internal ones (e.g., operations,
planning, and engineering) and external ones (e.g., investors,
customers, and regulators), have different perception of the created
values.
Need to accommodate multiple uncertainties both in future equipment
performance and service and system requirements in order to best
evaluate and manage risk.
Difficulty of aligning individuals, who make decisions and implement
programs with their own perspectives, to higher-level corporate
objectives.
Difficulty of aligning actions focus on individual assets to
higher-level corporate objectives.
In addition to these systemic issues, and in part a consequence of them,
no consensus path to power delivery asset management (PDAM)
implementation has emerged.
To help address these challenges, EPRI initiated a series of efforts to provide information and guidance to assist power delivery organizations in understanding and applying asset management principles. These efforts first resulted in Guidelines for Power Delivery Asset Management: A Business Model for Program Implementations, EPRI, Palo Alto, CA: 2004.
1008550. The following year, the work was enhanced with the publication of Guidelines for Power Delivery Asset Management, EPRI, Palo Alto,
CA: 2005, 1010728. In subsequent years, EPRI work focused on addressing the needs for analytical tools and equipment performance databases identified in these guidelines.
With the successful development and acceptance of a number of EPRI analytical tools and databases and the increased appreciation of asset management principles for guiding power delivery organizations, it was appropriate to revisit the AM Guidelines to be certain that they fully addressed current utility needs. The result was Asset Management Guidelines Development: 2018 Update. EPRI, Palo Alto, CA: 2018.
3002012681.That guide was further updated with utility supplied narrative descriptions of two different approaches to developing transmission substation asset management programs and some lessons learned from their implementations in Power Delivery Asset Management:
2021 Update, EPRI, Palo Alto, CA: 2021, 3002021198.In accordance with EPRI’s continuing efforts to provide members the most useful and up to date asset management resources and to make certain that they fully address current utility needs, this 2024 report documents the results of work to produce the next version of a transmission asset management guide.
Power Delivery Asset Management
The principles of asset management [3] will be discussed in detail in the following chapter, but some explanation at this point will be helpful in understanding the scope of work reported here and how the existing Guidelines were developed. Although it is expected that the majority of readers will be concerned about asset management of transmission equipment, the principles presented here apply equally to distribution assets. Consequently, this report used the broader terms Power Delivery Asset Management (PDAM).
Asset management is a systematic, consistent, and repeatable approach that integrates people, procedures, processes, and technologies to help the organization achieve its strategic objectives. In essence, asset management is the establishment and execution of a series of interrelated processes that ensure that all decisions related to the allocation of resources are evaluated against, aligned with and made to optimize the achievement of the organization’s goals for financial and operational performance and tolerance for risk exposure. Some key characteristics of PDAM are:
Policy, goals and objectives aligned with a strategy designed to
drive performance
Documented plans, processes and procedures to drive decisions on
resource allocation and application based on those policy, goals and
objectives
A longer term, proactive perspective of cost and performance and
future risk exposure
A mature asset management implementation needs a well-defined strategy,
plans to implement the strategy and track progress, processes, and procedures to implement the plans and manage data registries. For asset management to be most effective, processes must be in place to guide resource allocation decisions. For example, for maintenance and replacement programs to be effective there must be processes to ensure the direction over time tracks an organization’s mission and goals.
Chapter 3 is aimed at explicitly identifying these processes, the relationships among them and the data required for their full implementation.
Drivers for PDAM
The central area of conflict for power delivery organizations is defined by the opposing objectives of cost containment and preservation, or in some cases improvement, of the quality of service. These goals must be accomplished in the presence of three key constraining issues.
Aging Asset Base
Many in the industry believe that a significant percentage of the power delivery equipment installed base is at or close to the end of its useful life, the so-called design life. There is no formal definition of design life, but the general usage is that it is the age beyond which the risk of failure will become increasingly unacceptable. The concept arises from the fact that the original equipment designers and purchasers did not expect the equipment to be in service much beyond that age. Utility equipment engineers commonly use an age of forty years to estimate design life for many power delivery assets but there is no technical basis for this number and there are many examples of equipment functioning reliably well beyond that age and, of course, many that never achieved age forty. Nonetheless, it is well accepted that the risk of equipment condition deterioration and wear-out failure increases as equipment is used and ages and approaches some defined end of its useful service life. The concept of service life is further discussed in later chapters.
Many of the power delivery systems in the United States experienced a rapid expansion in the 1960s, 1970s and early 1980s corresponding with significant national economic growth and increased electric consumption.
Much of the equipment installed in that timeframe is still in service and the equipment installed during that peak expansion is now 40 to 50
years old. A great deal of the recent literature concerning aging transmission assets deals with power transformers because they are usually the single most expensive component in the delivery system, but the same situation exists for other power delivery system equipment.
Replacing this significant population of older equipment will require a large capital investment and hence the interest in managing the aging asset problem and related financial planning.
Resource Limitations
Constraints on funding for maintenance and replacements are well understood and require no further explanation, but there are other resource limitations also motivating asset management implementations.
In the last decade there has been a net decrease in the utility labor force. In step with the power delivery industry’s rapid expansion of the equipment base in the 1960s and 1970s, a similar increase occurred in the size of the workforce. More recently, the economic pressures resulting from business changes have resulted in a significant reduction in the number of people employed in the utility industry, both skilled craftsmen and engineers.
This reduction, coupled with the retirements of those remaining, means that skilled technical, craft and engineering expertise is in increasingly short supply both within utilities and supporting organizations. This shortage exacerbates the challenges of dealing with an aging asset base and the ever more complex issues of operating and maintaining systems under pressure to contain costs, accommodate new loads and maintain quality service. Consequently, most utility equipment experts are fully engaged in dealing with emergent problems and system growth and have less time to assess equipment fleets and improve maintenance, replacement and risk mitigation programs. Similarly, there are resource limitations on gathering detailed historical and condition data and on-site inspection and testing results one would ideally wish to review to assess an equipment population.
Increasing Operational and System Challenges
Building interest in electrification for a clean energy transition,
efforts to increase regional interconnections, expanding renewable energy sources, new capacity requirements for data centers and other large loads, climate change, unprecedented manufacturing lead times, and a desire for grid modernization are among the most pressing challenges facing utility managers. These challenges present new and complex issues that require significant management attention. Dealing with these matters successfully, especially with the other two constraining issues described above, requires a well established and executed strategy.
Project Scope
The objective of the work reported here is to review and update the existing Guidelines and provide any identified additions that will assist utilities in adopting best practice power delivery asset management.
PDAM recognizes that, at the highest levels, asset management processes should be very similar for most utilities. However, PDAM also recognizes that the approach to and application of these processes will differ from organization to organization due to individual circumstances, including the wide range of customer requirements, electric infrastructures and organizational structures. The utility implementation examples included in Chapters 8 and 9 illustrate this point and additionally describe specifics not appropriate for the generic models presented in Chapter 3
of this report.
In the Guidelines, PDAM is presented as a broad approach suitable across the power delivery industry but also recognizes and accommodates important distinctions, such as between transmission and distribution.
From one perspective, there are many similarities between transmission and distribution systems. The major equipment types are the same: lines,
transformers and breakers. The maintenance objectives are also the same:
maintain the desired performance levels as safely and economically as possible, but there are some important differences. Distribution equipment is much less expensive. The lower replacement costs make it hard to justify the level of maintenance, monitoring or testing that transmission equipment warrants. Distribution stations are configured differently and are obviously closer to the customer. Problems in distribution systems have a greater chance of affecting customer reliability indices and switching options are often more limited. Most distribution stations are unmanned and often remote.
There are differences in the basic equipment designs also. Because of their smaller sizes, commodity marketplaces and lower prices, design margins may be less in distribution equipment. These, and other reasons,
mean that PDAM focused maintenance and replacement programs would be different for both classes. After all, the drivers and constraints are different. In addition, the fact that there are such large numbers of very similar equipment in distribution may provide a better possibility of developing statistically meaningful hazard rate and life expectancy curves based on real-world data. A larger utility may have sufficient data for meaningful distribution equipment life analysis using only internal data. But even large utilities may require pooled data, such as from EPRI’s Industry-wide Databases, for transmission equipment life analysis. This topic will be addressed in more detail later. Another differentiator and identified area for additional effort is the development of performance metric for transmission equipment that would parallel the familiar SAIDI and SAIFI so useful in distribution.
The older Guidelines discussed risk assessment and management in the broadest context of asset management but did not provide detail.
Similarly, performance measures were reviewed at a higher level.
Additional detail on both topics has been identified as valuable for PDAM and candidate material for each is presented in this report.
The unbundling of power delivery functions, discussed in earlier Guidelines versions, has not progressed at the pace envisioned in 2005
and some adjustments in guideline terms have been made as a result.
Report Organization
This report is divided into chapters. In addition to this introductory chapter, included are:
Chapter 2: Power Delivery Asset Management Overview
Chapter 3: The Power Delivery Asset Management Model
Chapter 4: Maintenance and Power Delivery Asset Management
Chapter 10: Conclusions and Recommendations for Additional Work
This is a lengthy report, and the author recognizes that most readers will not peruse it in one sitting but rather over a longer time. Other readers may only be interested in specific chapters or topics. To facilitate these expected uses, there is intentional overlap and some material repetition among some chapters. This is designed to aid reading and retention for the more likely applications of this report.
1.2 - Chapter 2 - Power Delivery Asset Management Overview
This chapter outlines the basics of power delivery asset management and describes the principles and fundamental processes of PDAM. For the foreseeable future, utilities will need to manage an array of potentially conflicting business objectives, including the need to maintain competitive economic performance, improve customer satisfaction, maintain high reliability, address regulatory uncertainty,
and comply with increased environmental regulation. The result is that many utilities are considering or have moved towards implementing informal or formal asset management concepts and driving decision-making based on minimizing equipment life-cycle cost and risks and maximizing benefits. A structured asset management approach has been successful in many other industries and, when properly adapted to utility needs, can provide the framework, processes and tools to develop the most effective programs for building, operating and maintaining today’s power delivery infrastructure.
Power Delivery Asset Management begins with the fundamental premise that all asset management decisions made by utilities should contribute to stakeholder values, as set forth in the organization’s goals and policies. PDAM provides the tools for and applies this premise in decision processes at every level of the organization. The resulting alignment of decisions with criteria and metrics derived from those charged with establishing the organization’s direction ensures that every asset management and resource allocation decision consistently supports the organization’s strategic objectives and delivers value to the stakeholders.
The concept of asset management has been fundamental to the business of electric utilities throughout their history. Companies have always endeavored to manage their assets, employees, capital, and equipment to deliver as much perceived value as possible, and these efforts have been highly successful. However, several aspects of the traditional ways of conducting business in the electric power industry have changed. At many power delivery organizations, senior management has become more engaged in setting detailed performance goals and objectives that are designed to incentivize staff and inform decisions at the operational levels.
Some organizations have unbundled traditional vertically integrated utility functions via the sale of assets or entire operations, or by redefining roles and responsibilities (see Figure 1). In some circumstances, the roles are assigned to different enterprises, while in other cases; organizations within the same enterprise now perform these functions. In some cases, formal service level agreements have been established to define the roles and obligations of the three parties,
especially through outside contracting for tasks previously performed internally. Even where no corporate separation has occurred, recognizing these distinct roles, however labeled, is helpful when exploring asset management concepts.
Figure 1: Defining utility management roles
Each of the distinct functions, whatever the particular organization’s labels, has a well-defined role. The “asset owner,” or senior management, is represented by the highest levels of management within the organization that owns or, in the case of governmental agencies,
directly controls the assets. The asset owner may or may not be a part of the organization that operates and maintains the assets. This level directly interacts with all key stakeholders (e.g. customers,
shareholders, regulators, employees and financial agencies) and asset managers. The senior level sets the business goals and policies,
parameters of risk, cost and performance, and the budget for the organization. For example, the asset owner sets annual capital and operating budgets, customer satisfaction measures, and risk tolerances.
When asset management practices are applied in an organization below the enterprise level, an operating unit for example, the senior management to which the organization reports carries out the role of asset owner.
(For convenience, this report uses the terms “asset owner” and “senior management” interchangeably since there is no distinction between the asset management roles of “senior management” or “asset owner.”) This level determines the operating context for the asset manager by setting high level goals, while focusing on corporate governance and goals,
regulatory issues, and other stakeholder relationships.
The “asset manager” develops the asset strategies and policies within the bounds set by senior management and directs risk management,
investment and maintenance planning (not work scheduling), and contract management. The asset manager sets the policies and procedures for the service provider(s) and decides how and where money is to be spent for both capital improvements and maintenance. For example, the asset manager sets feeder outage goals, equipment maintenance intervals, and replacement criteria. In short, the asset manager decides what to do and in which budget cycle to do it to support senior management’s or the asset owner’s goals and strategic objectives.
The “service providers” focus on the core skills of scheduling personnel to deliver programs efficiently and effectively to meet defined service levels. They provide and schedule resources to perform work on the assets. For example, service providers set maintenance staffing levels,
tool requirements, and work schedules. Rather than deciding where or how to invest budgets, the service provider decides how to do work. These tasks may be performed internally or contracted out.
As utilities developed new business models and the technology to support their engineering expertise to meet emerging challenges, there has been a gradual change in focus towards power delivery asset management practices. However, asset management is about more than maintenance or capital investment issues that are usually the first areas for attention. At its best, asset management represents the ability to understand and manage the trade-offs among risk, cost, and performance in order to optimize the financial and service contributions of the three distinct roles—asset owner, asset manager, and service provider—that result from an integrated approach to managing assets.
Transmission asset management is a systematic, consistent, and repeatable approach that integrates people, procedures, processes, and technologies to help the organization achieve its strategic objectives.
As will be described later in this report, a mature asset management implementation needs a well-defined strategy, plans to implement the strategy and track progress, processes, and procedures to implement the plans and manage data registries.
What Is an Asset?
An asset is any resource that is important to an organization’s functions and requires management. The organization’s assets are used to service and supply customers or to facilitate performing such services.
Asset owners direct the acquisition, operation, and maintenance of assets to provide and support service delivery. Therefore, an asset has service potential or future economic benefit. In the power delivery industry, physical assets such as transmission and distribution system equipment are the most commonly considered. However, the more comprehensive application of asset management principles might also consider time, people, data and knowledge, and know-how to be assets.
Fundamentally, an asset has intrinsic value that endures over time. The value may decrease as for aging equipment but also may increase as for data and the knowledge to use it. This implies that assets have both a useful and an economic life, which may not be of the same length. For practical purposes, only assets with significant value are considered in the asset management process. The management of financial assets is not within the scope of PDAM.
What Is Asset Management?
Many formal definitions and approaches to asset management have been developed. At its most basic level, asset management is a fundamental business activity that involves the effective use of resources, assets,
to create value. However, such a description is not particularly helpful in understanding PDAM. Asset management is difficult to define comprehensively simply because it has so many dimensions. Asset management is simultaneously a business philosophy, a series of processes, and a set of technical tools.
As a business philosophy, asset management:
Is an approach to managing the organization’s assets that is
organization-wide and considers both short- and longer-term
performance and risks
Strives to collect and understand data and information from all
aspects and departments involved with assets and their performance
Is holistic and applicable to all operational areas in an
organization
Is motivated by policy goals and objectives established on
objectively measured performance metrics
Considers a longer-term perspective on cost and infrastructure
performance and risk
Is broadly applied to all aspects of every element of the
organization
As a process, effective asset management:
Has senior management support and direction, i.e. governance
Develops policies, plans and procedures based on clear goals and
objectives and implements optimized programs and procedures designed
to support those policies and plans
Requires decisions on resource allocations based on review of
alternatives and their projected costs and performance
Requires that risks are fully assessed and managed
Develops and documents organizational roles and responsibilities
regarding asset management
Promotes consistent practices across various organizations within an
enterprise
Develops and documents plans for normal operations and unexpected
events that maintain the focus on goals and objectives
Is interdisciplinary, combining both engineering and economic tools
and processes so that business functions become an integral element
of operations
Requires effective communication within and outside the
organization, and established mechanisms for performance review and
adjustments to correct for deviation from desired results and
continuous improvement
As a set of technical tools, asset management:
Requires effective management systems
Requires the best available historical, current, and accurate
information on assets and asset performance
Requires the ability to assess current and future risks
Requires well-developed decision support analyses for evaluating
tradeoffs and prioritizing actions
Power Delivery Asset Management
Numerous organizations have published formal definitions for the asset management process. Following the development and adoption of infrastructure asset management, most definitions originate from overseas organizations concerned with public service oversight and specifically designed to be asset and industry neutral (e.g. the assets could be railroads as well as public housing).
Although certainly accurate, these definitions may be limiting for the purposes of power delivery asset management. Therefore, in the context of this report, power delivery asset management is defined as:
A structured, integrated series of processes to align all decisions with business goals and values and designed to maximize the life cycle benefits of power delivery asset ownership, while providing the required service performance and risk exposure levels and sustaining the system going forward.
This definition includes assets of any form and services of any
type.
PDAM is “structured” because asset management is accomplished with
documented and consistent processes and procedures. All decisions
can be related to and support the organization’s goals and policies.
PDAM “maximizes the life cycle benefits of power delivery asset
ownership” because the purpose of asset ownership is to produce
benefits for all stakeholders. Examining costs and benefits over an
asset’s lifetime helps ensure that all contributions are taken into
account.
PDAM “provides required service levels” because minimizing costs and
maximizing benefits are not the only considerations. Performance
service levels also must be considered for both the short and longer
term.
PDAM “provides required risk exposure levels” because resource
allocations are to be made with an explicit understanding of the
associated risks for achieving the desired benefits and service
levels.
PDAM “sustains the system” because a well-designed asset management
program considers both short-term and long-term projected
performance and risk considering all potential costs and benefits.
Asset Management Premise
Power Delivery Asset Management begins with the fundamental premise that all asset management decisions made by utilities should contribute to stakeholder values, as set forth by senior management in the organization’s goals and policies. PDAM applies this premise in decision processes at every level of the organization. The resulting alignment of decisions with criteria and value measures derived from the asset owner’s or senior management’s direction ensures that every asset management and resource allocation decision consistently supports the organization’s strategic objectives and delivers value to the stakeholders.
Consequently, PDAM should begin with a comprehensive process for defining organizational values (e.g., financial considerations and non-financial considerations, customer satisfaction, environmental stewardship, and risk). The definition of “organization” could be the entire enterprise, a regional unit or even a department. PDAM then provides a way of linking asset management decisions to these higher organizational objectives. The explicit and quantitative consideration of uncertainty should be included in this process of decision-making.
Properly applied PDAM assures consistency across time and across the organization. The strategic planning process is important for establishing and articulating values to drive both tactical asset management and long-term direction.
PDAM integrates these features into a decision-making approach that relies on analysis methods and good data. The result is a systematic approach to business decisions that helps utility managers organize,
structure, and evaluate the functions they perform, while managing the assets required to support those functions.
Best-practice asset management is about aligning important processes across the entire asset lifecycle to higher-level strategies and values.
The key is to optimize tradeoffs among various financial and non-financial performance metrics, not simply trying to manage risk or lifecycle cost. Asset management decision-making is guided by performance goals, draws from both economics and engineering, covers a long time horizon, and considers a wide range of assets. PDAM requires the cost and risk assessments among alternative actions and resources allocation strategies from both the asset and system performance perspectives. It also allows a more comprehensive, performance -based comparative analysis among projects.
Implications of an Asset Management Approach
Utilities considering asset management are attempting to move toward risk-informed, performance-focused decision-making that minimizes equipment lifecycle cost and maximizes lifecycle benefits. A structured asset management approach has been successful in many other industries.
When properly adapted to power delivery, such an approach can provide the procedures, processes and tools to operate and maintain the power delivery infrastructure. Within an asset management framework, the contribution and support of a higher level, over-arching strategy drives all risk management and asset capital and O&M decisions. The major challenge for power delivery asset owners, managers, service providers,
and operators is to align their decisions with these goals and objectives through the use of asset management tools and processes.
Within an asset management framework, all performance criteria are derived from goals and policies set down by the asset owner, and all decisions are developed to support those goals. The core PDAM competencies are in the decision-making processes. In optimizing performance across the entire asset lifecycle, the asset manager should be supported by integrated business processes and decision support tools aligned with stakeholders’ (especially the asset owner and service providers) values. The asset manager uses these processes and tools to manage asset lifecycles, and to manage the internal and external service providers. These processes and tools and their elements can be summarized under the groupings listed below.
Communications
Accurate and timely information flows to both owner and service
provider
Complete descriptive demographic data for all pertinent (higher
value) assets
Complete data for asset operation and maintenance
Quantitative asset and system condition, risk and performance
measures
Historical performance records
Metric to assess progress in achieving goals
Strategy
Evaluation of asset performance and influencing factors
Risk goals and measures
Evaluation of lifecycle costs and benefits
Tools to predict future performance
Planning
Engineering and economic analysis tools
Alternative analyses procedures
Project prioritization procedures
Evaluations to balance short- and long-term objectives
Implementation
Contract management
Results monitoring and reporting
Continuous feedback procedures
Benefits of Asset Management
Asset management may affect nearly every part of the power delivery enterprise, from planning, engineering, construction and maintenance to finance and information technology. However, asset management is not just another management reconfiguration. Instead, asset management is a specific approach to running the organization. It brings a particular perspective to the manner in which an organization conducts its existing procedures and develops new ones, applies expertise and makes decisions.
PDAM offers techniques and principles to use in planning, policymaking,
project selection, data gathering, program tradeoffs, and management system application, aligned with the organization’s higher-level goals.
Asset management benefits can:
Assure that all asset decisions are policy driven and aligned with
senior management governance
Install, maintain, and operate facilities most cost-effectively
Achieve desired performance levels
Optimize long-term benefit/cost ratios
Allocate available resources efficiently to support the
organization’s overall goals and policies
Measure and focus on performance and results
Improved repeatability, quality and accountability for decisions
PDAM links the customers’ and regulators’ requirements and expectations for power system availability and performance with system management and resource allocation strategies. A complete asset management process tracks progress made in achieving performance measures derived from the asset owner or senior management goals and also evaluates the business processes used relative to the goals and performance criteria. There are processes to project and evaluate the potential benefits and risks of alternative actions and resource allocation strategies on achieving the desired goals and objectives. The focus is on assets and system performance and the associated data, including operating and maintenance costs, risk exposure, and future resource requirements. This comprehensive approach can provide benefits to both the organization and its stakeholders.
In addition to aiding the decision-making process, asset management also supports fact-based communication among asset managers, asset owners and stakeholders. The availability of objective, credible and relevant information benefits all those participating in decision making.
Decisions can be based on documented current performance and estimates of future performance. The information supporting the asset management processes—both data and information results from analysis—provides decision makers and stakeholders with a better understanding of the return on investment, economic tradeoffs, accountability, and performance impacts of their decisions.
In addition, asset management offers access to data and information that enables decision makers to rapidly identify and focus on important issues. The approach enhances a decision maker’s ability to evaluate and communicate the possible results of selecting a preferred alternative.
The documentation explaining and justifying the choice of a given strategy also is improved. Such a systematic, documented and process-driven approach can improve communication to stakeholders and provide the asset manager with a defensible rationale for capital investments and other actions.
Requirements for Asset Management
Establishing effective PDAM systems and procedures requires effort,
investment, and a business commitment to developing, implementing, and maintaining an asset management approach and the elements needed to support it. Although PDAM can be accomplished in a phased implementation, the ultimate goal is to implement most or all of the following components:
A well-defined asset management strategy
Developed and documented policies and business practices, with
assigned responsibilities
Developed and consistently applied procedures, performance criteria,
and measures
Complete asset inventory
Quantitative condition and performance measurements
Performance prediction capabilities
A lifecycle view of costs, risks and benefits
A suite of engineering and economic analysis tools
Performance monitoring systems
Processes for performance review and adjustments
A key to effective asset management is good information – timely,
reliable, and accurate data to support the PDAM processes. Information technology, including work management systems, relational databases to integrate individual management systems, monitoring systems, databases,
and other analytic tools, should complement PDAM decision-making processes, as well as organizational roles and responsibilities. The utility example implementations presented in later chapters show how two different organizations addressed these requirements.
A Holistic Power Delivery Asset Management Process
To enable the broadest possible application, this guide has been designed to be equally useful to the many different forms of power delivery organizations. The material included is applicable to both public power and investor-owned utilities and organizations of various sizes. The business model is independent of an organization’s management structure or functional boundaries.
The power delivery asset management conceptual business model presented in the next chapter provides a visual representation of the functional elements of a complete PDAM implementation and the interrelationships of these elements. By introducing the fundamental elements and concepts of asset management, the guidelines are meant to serve as an introductory primer for modern asset management practices, present the initial steps to translating general asset management principles to the power delivery industry, and provide a starting point for transmission and distribution managers interested in understanding what modern asset management entails. The model itself serves to better define the PDAM processes and to identify data and analytical tool requirements for individual elements and processes.
Some Definitions
Before proceeding, it will be helpful to establish some definitions. As with many English words, the terms below have multiple definitions and some other authors may prefer one term over another or present them in a different hierarchal order. Below, each term definition is followed by an example. The associated definitions along with the example should reduce any confusion when comparing this report’s use of these terms to other documents.
Mission – A usually succinct statement of the organization’s purpose and core values
Assure our customers an adequate, economic, safe, efficient and reliable electric power supply while respecting our employees, the public, and the environment.
Vision - Describes, in ideal terms, a desired future state of the organization.
Provide electric service with the best distribution reliability in the state.
Goal - A broad statement of the long-term results needed to accomplish the organization’s mission and achieve its vision. Strategic goals are broad statements defining changes the organization hopes to achieve during the strategic planning horizon. Goals focus on outcomes or results and are qualitative in nature. Goals are defined as broad,
ideal future conditions, the results the organization wants to accomplish. As an example, one of the possible goals for distribution could be:
Reduce the number and duration of customer outages in every operating region.
Strategy - A strategy explains the way a goal or group of goals will be achieved. Strategies are statements of major approaches or methods for attaining each goal and resolving specific issues. Strategies may be supported by documented policies and procedures. An example strategy for the above goal is:
Improve the reliability of the ten worst performing circuits in each service area.
Objective - Strategic objectives flow directly from strategic goals.
They translate the quantitative expectations of goals into specific and measurable targets that the organization should meet to realize the goals. Objectives tell what the organization plans to achieve, who will do it, how to know when it is achieved, and what are the key performance indicators for tracking progress. Unlike goals, all objectives are expected to be met within the planning horizon. As an example, an objective to the above-stated goal to provide the best distribution reliability service in the area could be:
Have no feeder with a SAIFI greater than x by y year.
Plan- A documented description of the steps required to achieve an objective (a defined outcome). Plans may be supported by documented procedures. One possible plan to achieve the above objective is:
Detailed description of a project to replace X miles of feeder A by year B.
Summary of Asset Management Concepts
Decisions on asset acquisition or replacement, operation,
maintenance, and retirement that are driven by performance
requirements derived from the asset owner’s goals.
Resource allocation decision processes with evaluation of
alternatives that include a comparison of lifecycle costs, benefits,
and risks of ownership.
Clearly defined, documented and understood processes and procedures
that identify responsible parties for all aspects of assets and
system performance.
Performance monitoring and measurements against meaningful standards
established from quantitative objectives and consistently and
accurately recorded.
Chapter References
1. Guidelines for Power Delivery Asset Management. EPRI, Palo Alto,
CA. 2005. 1010728
1.3 - Chapter 3 - The Power Delivery Asset Management Model
One key goal of EPRI’s asset management work is to develop a visual representation of the functional elements of a complete power delivery asset management implementation and their interrelationships as shown in Figure 1. Modeling the PDAM conceptual implementation illustrates the interfaces among the various components and shows the required inputs and expected outputs. The model helps to better define the important PDAM processes and to guide subsequent development work to identify data and analytical tool requirements for the individual elements and processes.
For the broadest possible application, this model has been designed to be equally useful for many different forms of power delivery organizations (e.g., various different sizes of public power and investor-owned utilities). The model elements are independent of the organization’s management structure or functional boundaries or labels.
A broad approach was used in model development. Wherever possible,
organizationally neutral terms and process identifiers were used. The model is intended to be truly generic (e.g., no blocks are labeled “Engineering” or “System Planning”). Recognizing the likelihood of a phased implementation, the models can be as easily applied at the utility department level as at a section, facility, or even asset level,
with only modifications to the meaning of asset owner, senior management and other stakeholders. In each case, the functional representations for asset management concepts remain the same.
The power delivery asset management model in Figure 1 is neither strictly a process nor data flow diagram, but instead includes elements of both. The objective is to illustrate a conceptual representation of best practice PDAM without the constraints of a formal process diagram.
Equally important is the objective to describe PDAM at a level of detail that allows for specifications of functions, data and decision support tools to be developed or acquired.
Some liberties were taken in the degree of complexity shown for purposes of simplicity. Figure 3-1 purposely is not consistent in the level of detail among the various processes depicted. For example, the Analysis Performance Modeling and Prediction process combines together in one block many more sub-processes than the Develop Action Plan process. The purpose here is to show all the key functional concepts and interactions in order to provide a complete visualization for a PDAM implementation.
The intent is to provide a descriptive, not prescriptive, reference.
Other authors may combine and arrange the PDAM model elements differently, but the overall functions should agree. One important purpose of constructing this conceptual model is to illustrate the functional boundaries and data exchanges among the various processes,
wherever they may reside in any particular organization’s implementation.
Figure 1: The power delivery asset management model
The diagram in Figure 3-1 clearly indicates the responsibilities of the three parties discussed previously. As depicted, all asset-related decisions should be guided by the goals of the asset owner, senior management and other key stakeholders. The asset manager’s responsibility is to develop and implement strategies to direct resources to their optimal uses, as defined by the organization and its stakeholders. The service provider carries out the actions requested by the asset manager. Ideally, asset management applies at all levels, in all time frames, for capital investments as well as ongoing operations,
continually balancing different and often conflicting goals. Labels in the figure in bold text refer to diagram blocks. A brief description of some key aspects of the PDAM process will aid in putting the complete process in perspective. A more detailed explanation of each included element is provided later.
Goals and Policies
The asset owner (or senior management) is the initiator for PDAM. The owner provides the overarching governance by setting the business parameters, risk boundaries, and operating context for its assets for the operational and longer-term horizons. This level also sets the operating context for the asset manager and, for power delivery, focuses on corporate governance as the regulatory license-holder. In general,
the owner is represented by the highest levels of management within the organization that owns or, in the case of governmental agencies,
directly controls the assets. The asset owner may or may not be a part of the organization that operates and maintains the assets. Owners directly or through senior management interact with key stakeholders
(e.g., customers, shareholders, regulators, employees and financial agencies), as well as asset managers. The asset owner sets the Goals and Policies and the Budget for the organization.
The asset owner or senior management are responsible for the on-going governance of the asset management program and should develop and clearly communicate well-defined high-level Goals and Policies and a strategic framework for operating the organization in an asset management context. These goals should be translatable by the asset manager to clear business objectives and measures of performance and used to develop an asset management strategy. Some goals, such as desired internal rates of return, might be very specific and quantifiable. Other goals, such as improving customer perception, may be less specific and incorporated in a long-range plan resulting from a strategic planning process. It is the asset manager’s responsibility to translate these goals and policies into measurable objectives and to develop the strategies, processes and procedures needed to achieve them.
Included here may be an outline of business processes and organizational responsibilities and roles reflecting the asset owner’s policies and philosophies. These goals and policies often start with a mission statement and are used to set performance metrics for subsequent processes.
The policy formulation process should seek input from the various stakeholders, and reflect customer priorities and concerns. Stakeholders can include the asset owner’s parent company, shareholders, regional operating organizations, state and federal utility, safety and environmental regulators, local governments and the public at large.
Each of these groups may have different goals and metrics. Some may be very specific and short term, such as specific earnings per share target or an availability factor. Other goals and metrics may be less well-defined or longer term. These stakeholders (particularly regulators) also may have different constraints on actions that can be taken. These are “must do” or “must not do” types of inputs. The asset owner or senior management provides the governance to establish how to weigh tradeoffs between competing interests and produce a consolidated set of goals and operating policies.
Performance Assessment
Assessment of asset and system condition and performance provides quantitative data and information about the performance of the Asset Inventory in meeting established Performance Criteria and information that can be used in subsequent analyses to predict the ability to meet these requirements in the future (an important input for maintenance process planning and life cycle management). Asset condition assessment and performance measurements, including risk and cost, and tracking form the information source for asset life cycle management.
They provide a basis for adjustments to the various outputs of subsequent processes, ensuring that expected performance goals are met and providing indications where changes are required. Effective asset condition assessment in relation to its service performance is needed to understand and predict the deterioration that leads to reduced asset performance in the future. Assessments of system performance and implementation also may be conducted by external stakeholders (e.g.,
customer perceptions of infrastructure condition, or regulator assessment of the provision of services), and these are valid inputs for analysis as well. These evaluations are a key process of PDAM.
Understanding the current condition of an asset or the current level of performance, including risk, provided by a system provides vital information for a series of asset management maintenance decisions and a starting point for predicting future performance.
Determining the current level of system performance usually entails straightforward calculations, such as summing the number of customer interruptions or corrective maintenance work orders. Tracking equipment condition parameters has a number of uses. The most obvious use is for deciding whether some immediate corrective action is indicated. For equipment, many of these evaluations are part of normal maintenance activities. However, it is important that the history of these activities not be “islanded” in the maintenance system. Rather, this information should be available to the larger PDAM process, of which maintenance is just a part. In addition to triggering maintenance, such information should be used to determine how well past asset management decisions have been implemented and whether the expected improvements have resulted. This assessment information also can provide a starting point for projecting future asset or system condition through the development of deterioration models and also to refine existing models.
Explanation of Model Elements
Model Input/Output Definitions
D1 –Senior Management or Asset Owner
This functional level sets the business parameters, risk boundaries, and operating context for its assets. The asset owner or senior management also sets the operating context for the asset manager and, for power delivery, focuses on corporate governance as the regulatory license-holder. In general, the owner is represented by the highest levels of management within the organization that owns or, in the case of governmental agencies, directly controls the assets. The asset owner may or may not be a part of the organization that operates and maintains the assets. Owners directly or through senior management interact with key stakeholders (e.g., customers, shareholders, regulators, employees and financial agencies), as well as asset managers. The asset owner or senior management sets the high-level Goals and Policies and the
Budget for the organization and thereby provides the direction and governance for the asset management program.
D 2 - Customers and Regulators
Customers and Regulators include all recipients of the services provided by the organization or organizations that own and/or operate the Assets and all regulatory bodies, including local, state and federal, that can influence operation of the organization, regional transmission operators and the general public. The service levels provided with the assets directly impact customers and RTO’s.
Regulators, through formal reporting and customer feedback, monitor service and asset performance and can mandate actions directly impacting the organization’s performance requirements. Regulators also are influenced by and can in turn affect the Financial Markets’
perception of the organization.
D3 - Financial Markets
Financial Markets are the markets that the Asset Owner or Senior Management uses to obtain both short- and long-term financing. These markets and associated financial institutions are aware of the organization’s service levels and asset performance, as well as the traditional business financial indicators. The markets also interact with the Regulators as described above indirectly through bond ratings and stock valuations. Financial markets can heavily influence the Asset Owner or Senior Management’s Goals and Policies for privately owned utilities.
D4 - Goals and Policies
The Senior Management or Asset Owner should develop and clearly communicate well-defined high-level Goals and Policies and a strategic framework for operating the organization and generating an asset management strategy for the asset management function. These goals should be translatable, in conjunction with the asset manager, to clear business objectives and measures of performance. Some goals, such as desired internal rates of return, might be very specific and quantifiable. Other goals, such as improving customer perception, may be less specific. It is the asset manager’s responsibility to translate these goals and policies into measurable objectives. Included here may be an outline of organizational roles and responsibilities and business processes that reflect the asset owner’s policies and philosophies.
These goals and policies often start with a mission statement and are used to set performance metrics for subsequent processes.
Policy formulation seeks input from various stakeholders, and reflects customer priorities and concerns. Stakeholders can include the asset owner’s parent company, shareholders, regional operating organizations,
state and federal utility, safety and environmental regulators, local governments and the public at large. Each of these groups may have different goals and metrics. Some may be very specific and short term,
such as specific earnings per share target or an availability factor.
Other goals and metrics may be less well-defined or longer term. These stakeholders (particularly regulators) also may have different constraints on actions that can be taken. These are “must do” or “must not do” types of inputs. The Asset Owner relies on the leadership,
vision, values, business objectives, and judgment of the organization and its senior management to establish effective asset management strategies.
D5 - Capital O&M Budgets
The Senior Management or Asset Owner, at the highest levels determines the organization’s financial framework, sets both capital and operating budget limits taking into account desired operating service levels, revenue forecasts, regulatory constraints, costs of capital,
financial goals and performance, and expectations of the markets where applicable. These are the financial boundaries for the execution of the asset management strategy. Public power organizations have different processes for arriving at the available funding levels, but senior management would still be responsible for setting budget limits. It is the responsibility of the asset manager to inform senior management of any conflicts between available funding and expected asset or system performance.
D6 – Assets
The organization’s Assets service and supply customers or facilitate delivering such services. The reason Asset Owners acquire, operate,
and maintain an asset is to support service delivery. Therefore, an asset possesses current service potential or future economic benefit.
For power delivery, physical assets such as transmission and distribution system equipment are the most commonly considered. However,
a more comprehensive application of asset management principles might also consider time, people, data, and knowledge as assets to be optimally managed. The common understanding of an asset in any context is that it has value that persists over time. This implies that assets have both a useful and an economic life, which may not be of the same length. For practical purposes, only assets with significant value are considered in the asset management strategy and processes. Different organizations may have different value thresholds.
D7 - Asset/Service Performance
Asset and Service System Performance represents the final output of all the processes of the organization. These terms are used in the broadest sense, and there are two distinct but related aspects to asset/service performance. The organization’s Assets are used to provide services to customers – end user service levels, often referred to as system performance in power delivery – and these can be measured by various metrics. Examples for power delivery include SAIFI, SAIDI,
and energy-not-delivered and these are developed in the Evaluate Asset Condition and System Performance process. In addition, individual assets or groups of assets are expected to perform at certain levels,
which may or may not affect end user service levels. Examples here include equipment availability, failure rates, return on investment, and maintenance costs. The service levels provided with the assets directly impact customers and are directly influenced by Asset condition and operating procedures.
D8 - Asset Information and Inventory
Asset Information and Inventory includes a collection of databases or data stores that hold complete, current, and accurate information on system performance, asset condition and performance metrics, risk,
failure and replacement histories, asset location, age, specifications,
and costs. An Asset’s Performance can only be accurately established in the total context of its use. Therefore, data on operations and maintenance histories and costs, as well as the asset’s functional importance to system performance measures should also be recorded. A mature asset management implementation will also include projected future asset condition and risk.
D9 - Risk Definitions Performance Criteria
Risk Definitions Performance Criteria is the set of qualitative and quantitative values that were developed in the Establish Review Performance Requirements process. They set performance goal levels and risk boundaries that support the asset owner’s goals and should be addressed in the asset management strategy. Power delivery risk is discussed in more detail in Chapter 6.
D10 - Standards and Regulations
Regional operating organizations and system operators, state and federal utility, safety and environmental regulators, and state and local governments may impose conditions that constrain the development,
definition or timing of various action plans. The asset manager in the
Develop Options process must consider these Standards and Regulations.
D11- Cost Data
Cost Data is the information needed to price developed options accurately and manage on-going asset life cycles. Data comes from external suppliers and service suppliers, asset records, and internal cost accounting sources.
D12 - Proposed Actions
Proposed Actions are a set of activities that have resulted from the
Develop Options process to address identified service or performance shortfalls or new goals. Possible actions include repair or replacement of an existing asset, addition of new assets, and changes and additions to operating or maintenance procedures. Each proposed action is associated with a complete description, estimates of costs over its lifecycle, and evaluation of expected benefits and risk reduction. More than one alternative may be proposed to meet a specific need. Competing alternatives are analyzed in the Evaluate Alternatives process.
Asset life cycle management plans may be in place for some groups of assets, and these can provide action guidance.
D13 - In Progress Projects
In Progress Projects include projects continuing over from previous years or planning cycles that are in progress and expected to be continued, if they are not displaced in the Program Optimization
process.
D14 - Emergent Work
Emergent Work is all emergency or unanticipated work that must be conducted. Timely response may require direct input to O&M Projects,
but should be accounted for in the Develop Action Plan process also to assess budget or resource impacts on other work.
D15 - Unfunded
Unfunded projects or other actions have a ranking as determined by the Optimize Program process such that a limited financial budget or other resource limitations precludes their implementation (i.e., “below the line projects”). They may be deferred or resubmitted after final review of the risk of not proceeding in the Assess Risk decision process.
D16 - Budget Requirements
Budget Requirements is the sum of all funds required to implement the results of the Develop Action Plan process. The business needs for funds may exceed the original allocation. In that case, this output would show the shortfall and, carried through from previous processes,
the associated risks and potential benefits for considering budget changes by the Senior Management or Asset Owner.
D17 - Tactical O&M Projects
Tactical O&M Projects is the list of actions that address short-term and O&M issues. This includes routine maintenance and testing, as well as possibly some capital work such as equipment installation that may utilize the same resources. For simplicity, included here implicitly is the work management process that evaluates all current and emergent work for assignment, determines resource requirements, requests outages, and develops schedules. The Service Provider may perform some of this work scheduling.
D18 - Strategic Capital Projects
Strategic Capital Projects is the list of actions that are considered to be capital investments. Capital and O&M workflows should be coordinated in order to maximize efficiencies and minimize resource constraints.
D19 – Mandates
Mandates include all legal, government, or contract obligations that may or may not be performance driven in the sense that they align with explicit, internal performance criteria. Such mandates often bypass the
Develop Options process, but in some cases, can be addressed in more than one fashion. In the latter case, possible options should be developed.
D20 - New Business
New Business is demand growth that may require new assets or system capacity expansion. For power delivery, this is load growth or new customers. New demands are analyzed in the Analysis Performance Modeling and Prediction process to determine whether or not some action is required to maintain or achieve the desired performance levels.
Model Process Definitions
P1 - Monitor Assets and Performance
Monitor Assets and Performance is the process of collecting data that reflects Asset and System Performance and condition. The actual scope of the data collected is determined by the asset management strategy. There are multiple processes, both manual and automated, used to monitor and measure both end user service levels and the performance of individual components, assets, or groups of assets. Inspections,
testing, on-line monitors and trouble call tallies are examples of these monitoring processes. The outputs of these monitoring processes are stored in various databases, the Asset Information Inventory,
throughout the organization for analysis, direct reporting, and subsequent use in other processes. Only data that has an identifiable use in a subsequent process should be collected. The adequacy
(timeliness, precision, etc.) of the data outputs of this process should be corrected by feedback from all subsequent processes that make use of the data (not shown).
P2 - Establish Performance Requirements
The Goals and Policies of the Senior Management or Asset Owner
are used to develop criteria for decision-making and measurements of asset and system performance towards achieving those goals. These should be well documented in the asset management strategy. To accomplish this,
performance indicators, specific qualitative or, preferably,
quantitative measures that allow performance against a benchmark to be assessed, are required. This step involves transforming the high-level strategy of senior management into a set of decision criteria for evaluating actions at the lower levels. These criteria could be numerical, such as average restoration time requirements, or they could be qualitative, such as a decision that a safety related task has the highest priority. They range from internal business and engineering indicators to customer and financial perspective indicators. Whatever form they take, the criteria and performance requirements should be constructed and documented to allow the asset manager to use them for prioritizing activities in a manner that is consistent with the high-level strategy, goals, and policies of the senior management or asset owner. The next step after establishing performance criteria in this process is risk identification and an assessment of constraints,
which define success criteria and unacceptable risk. The impacts from requirements for regulatory compliance are also included here.
P3 - Evaluate Asset Condition and System Performance
The Evaluate Asset Condition and System Performance process takes inputs from the Monitor Assets and Performance process and either directly or with added calculations compares them with desired levels from Risk Definitions and Performance Criteria. Gaps or anomalies in performance levels may be identified here. Additional input comes from the Asset Information Inventory, to put the monitoring data in context. The major output is a series of values that reflect the condition and risk assessments of the system, groups of assets,
individual assets, or asset components. Examples are outage numbers and durations, number of equipment failures, and cost totals for a specific number and kind of task. This process addresses directly measured performance metrics and values with the objectives of determining current asset condition and performance levels and providing data for calculating risks.
Evaluations of asset and system performance provide factual and quantitative information on the performance of the Assets in meeting established Performance Requirements and information to predict their ability to meet these requirements in the future. Performance monitoring forms the basis for management of an asset throughout its life. It facilitates adjustments to the various outputs of subsequent processes, ensuring that program performance goals are met and providing indications where changes are required. Effective asset condition assessment in relation to its service performance is needed to understand the deterioration that leads to reduced asset performance in the future. Evaluations of system performance also may be conducted externally (e.g., Customer perceptions of the quality of infrastructure condition, or Regulator assessment of the provision of services), and these are valid inputs for analysis as well.
P4 – Analysis Performance Modeling and Prediction
Performance modeling entails analyzing performance data and asset information from the Evaluate Asset Condition and System Performance
process in order to predict the future condition and associated risk of an asset, subsystem, or the complete system and how it may respond to future demands or stresses. This process is concerned with deriving,
calculating, and analyzing indirect performance metrics and values including risks. It works on identifying and understanding the causes of performance gaps, current risks and trending, and predicting future performance and risk. The general objective here is to predict future asset condition or system performance levels.
Modeling performance generally requires data on past performance of similar facilities or equipment and some understanding of the mechanisms of aging and wear that contribute to a decline in performance over time.
Knowledge of how operating stresses may influence asset degradation over time (i.e., aging models) is useful in this process as are historical failure records. Expected future operating conditions are also required,
including New Business. Analysis may also be required to identify the underlying causes of performance gaps or an unexpected asset condition through root cause analysis. Using various analytical,
statistical, and simulation tools, this process determines such information as statistical failure data, predicted end of life,
condition-based triggers to support proactive asset maintenance or replacement, the implications of deferred maintenance, probability and consequences of failures and other risks, and future rating limitations.
A mature asset management implementation would include processes for evaluating model results and predictions and making any necessary changes.
P5 - Develop O&M Strategies
The Develop O&M Strategies process develops operating and maintenance strategies and practices that are to be generally applied in the organization. Based on review and analysis of Risk Definitions Performance Criteria and risk assessments, industry practices,
manufacturer recommendations and historical data, this process’s outputs include maintenance procedures, value and timing of preventive maintenance tasks, condition-based maintenance triggers, maximum operating levels and other similar standards. This process includes reliability-centered maintenance analyses and updating and other maintenance plans in addition to the operations and maintenance stages of an asset life cycle plan. Maintenance that is not condition based goes directly to the O&M action plan process. This process translates performance criteria to task level activities. Equipment failures are analyzed here and result in one-for-one replacement (R2 Continue) or, if necessary, development of new options (P7) or new procedures.
P6 - Develop Capital Strategies
The Develop Capital Strategies process develops capital strategies generally applied in the organization. Based on review and analysis of
Risk Definitions Performance Criteria, performance and risk assessments, costs and other economic consideration, this process’s outputs include replacement triggers, spare unit criteria, and lifecycle costs modeling criteria.
P7 - Develop Options
The Develop Options process develops a set of Proposed Actions
to address identified service or performance shortfalls or new requirements. These options are cost-effective alternatives for possible implementation compatible with the operating and maintenance and capital strategies of the organization. The options include additions, upgrades,
repair, or replacement of an existing asset; addition of new assets; and changes and additions to operating or maintenance procedures. Each proposed action (above some cost threshold) includes a complete description, estimates of costs over its lifecycle and lifecycle performance, and an evaluation of expected benefits and risk reduction developed in this process. More than one alternative may be proposed to meet a specific need, and a range of alternatives may be proposed for some performance issues. Competing alternatives are analyzed in the
Evaluate Alternatives process. This is essentially an engineering and design process for power delivery and both capital and O&M are considered here.
P8 - Evaluate Alternatives
The Evaluate Alternatives process evaluates Proposed Actions
where several alternatives have the same objective. For example, in power delivery, a task may be to choose the best combination of preventive maintenance and testing tasks to keep a population of equipment operating at a specified level of reliability or to meet new load demand by upgrading an existing station or by adding a new station.
The objective is to evaluate a number of possible project alternatives over a long period for a segment of the asset population, subject to defined conditions and limitations, and select the strategy that has optimal value based on the desired performance criteria and risk boundaries. This process relies on calculation of lifecycle costs and benefits, including risk reduction, so that alternatives can be properly evaluated. Valid economic models; data for acquisition, installation,
operation, maintenance, and disposal costs; and quantification of benefits are needed.
P9 - Optimize Program
The objective of the Optimize Program process is to establish priorities between competing uses of resources for multiple objectives.
This process evaluates Proposed Actions and the selected results from Evaluate Alternatives in order to identify the optimum mix
(maximum total value, highest benefit/cost ratio, some qualitative metrics) of different possible projects that produce different kinds of benefits, while not violating budget and other constraints and limitations. The optimal project mix should attempt to achieve minimal total cost, while maximizing the aggregate benefits resulting from the project mix.
The process takes the Proposed Actions and prioritizes and ranks them according to the criteria developed in Risk Definitions Performance Criteria. Also included in the inputs are In Progress Projects carried over from previous years or planning cycles, which are to be evaluated. The key to successful asset management at this phase is the ability to evaluate candidate projects using a collection of attributes that describe financial and system performance implications in a consistent and logical basis with a process that fairly treats projects with different attributes, different time horizons, and responses to different customer and system needs and that is aligned with stated policy objectives and performance measures and targets. The process selects the best multi-year program of projects given a series of constraints and presents the implications of changes in budget levels when evaluating projects with different attributes.
The output of this process is a ranked action list that consolidates all projects and evaluates them relative to each other. The Develop Action Plan process uses the list to establish what projects will be conducted, given strategic goals and constraints related to long- and short-term planning and budgeting.
P10 - Develop Action Plan
The Develop Action Plan process takes the output from the Optimize Program process and produces grouped plans of actions for implementation. This process “rolls up” all successful candidate actions and associated budget requirements. It identifies a set of actions that can be implemented, given a range of available resources, and a recommendation of what actions should be implemented given anticipated resources. This is the proposed set of priorities submitted for approval. Included here is the final approval process for capital investments. Emergent work also needs to be considered in the action plan in order to assess budget and resource impacts and make adjustments for other projects if necessary. However, some circumstances may require rapid responses that may necessitate immediate action. In such cases,
such consideration may take place after the fact.
P11 – Implementation
Implementation represents all actions to undertake efforts associated with acquisition, installation, maintenance, refurbishment,
replacement, and disposal of assets. In asset management terminology,
this process is the main responsibility of the service provider. The service provider may be different for different actions or equipment and may be part of, or separate from, the asset manager’s organization. This represents the main interface between the asset manager and the service provider(s). Included here are work and contract management, service level agreements by the asset manager and task design, work planning and scheduling by the service provider.
P12 - Monitor Implementation
The Monitor Implementation process monitors the work done to implement the approved actions (not the assets). At one level, it is a check on the service provider, but it is also important to the larger asset management focus. It includes tracking of actual delivery costs as measured against expected costs. This information can be used to improve understanding of the true costs of various activities so that this information can be used to enhance future resource allocation decisions in Proposed Actions and in a few cases may necessitate review of proposed actions. Similarly, schedules are also monitored. Problems in implementation may necessitate changes in the Develop Action Plan
process.
Model Decision Point Definitions
Q1 - Meets Future Requirements
A “yes” result for the Meets Future Requirements decision process means that, over the future planning period of interest, asset or system performance is predicted to meet all necessary requirements and that no changes need to be considered. A “no” result necessitates an evaluation of the future risk exposure in order to determine whether action is required.
Q2 - Meets Current Requirements
A “yes” result for the Meets Current Requirements decision process means that asset or system performance meets all necessary current requirements and that no changes are required. A “no” result means that action options must be developed to meet the performance gap.
Q3 - Assess Risk
A “yes” result for the Assess Risk decision process means that any risk associated with potentially not meeting future performance requirements for an identified criteria resulting from the Analysis Performance Modeling and Prediction process or with not implementing unfunded actions resulting from the Develop Action Plan process is low enough to defer action. A “no” result requires further action.
Model End Point Definitions
R1 - Defer
Actions identified in preceding processes that will not be implemented end in the Defer end point. This is the result of the Assess Risk decision process when 1) assessment of the risk associated with a predicted future performance gap indicates that no action is necessary,
or 2) the review of Unfunded projects or other actions whose ranking as determined by the Optimize Program process is such that limited financial budget or other resource limitations preclude their implementation.
R2 - Continue Current Practices
When the results of Analysis Performance Modeling and Prediction
indicate that risk exposure is acceptable and no changes or additions are required, current practices can be continued. This may occur, for example, when analysis shows that an unexpectedly high equipment failure rate was due to circumstances not likely to reoccur or that they will be mitigated by some other action.
Asset Management and Asset Life Cycle Management
The preceding discussions have made many references to asset life cycles and described how important proper life cycle management is to good asset management. Many graphical representations of asset management include some depiction of the stages of an asset’s life, i.e. the life cycle. The granularity and labels for these stages differ but a representative list would be:
Planning and budgeting
Acquisition
Operations and maintenance
Disposition
The decisions made around each life stage and how and when to transition among them constitute asset life cycle management.
Often asset management diagrams include some circular or wheel-like representation of the concept of an asset life cycle with the stages distributed around the circumference, but the reader may have noted that no such representation appears in the PDAM model in Figure 1. However,
all of the decisions around the stages of an asset’s life can be mapped into processes in the model, namely:
Monitor Assets and Performance - P1
Evaluate Asset Condition and System Performance - P3
Develop O&M Strategies - P5
Develop Options - P7
Optimize Program - P9
Implementation - P11
The author purposely has chosen not to use the more common graphic to emphasize that, for proper asset management, it is important not just that decisions are made around each life stage but rather more so how and on what basis they are made. Clearly, asset life cycle decisions have been made long before the principles of good asset management were established. Proper asset management practices dictate that these decisions be made to best balance costs, risks, and performance in accordance with the asset management strategy. Each decision should be based upon considerations of the resulting performance, risk, and life-cycle cost impacts.
As a side note, a circular representation of a life cycle implies that the asset life cycle repeats but, of course, once the asset is removed it is gone from consideration. What actually more likely repeats are the functional requirements for the asset and the processes needed to make decisions around each life stage. The strategy, goals and objective related to an asset’s performance requirements may change over time so the drivers for the related decisions – life cycle management – also may change.
Nonetheless, life cycle management plans are an integral part of best practice asset management when they are developed based on the established strategy, goals and objectives and performance requirements for the asset. Asset management plans can be an effective and efficient means to make certain that the decisions around each life stage of an individual asset and the transitions among them are aligned with the organization’s objectives. Developing such plans for asset groups or fleets reduces the burden of making individual asset decisions and ensures more consistent results. Life cycle plans are not the same as maintenance plans. Maintenance plans only pertain to assets in their operating and maintaining stage. Maintenance plans will be discussed further in Chapter 4.
Life cycle management plans should be developed with considerations of the performance, risk and costs of each decision. In addition, life cycle management plans should be associated with processes that identify and review shortfalls in asset level performance to evaluate the potential impact of or need for changes to the plan and periodic reviews to evaluate plan adequacy. The goal is to optimize at the group or fleet level trade-offs across a variety of financial and non-financial metrics, rather than simply attempting to manage life cycle cost or risk at the individual asset level.
Chapter References
Guidelines for Power Delivery Asset Management. EPRI, Palo Alto,
CA. 2005. 1010728
1.4 - Chapter 4 - Maintenance and Power Delivery Asset Management
The previous chapters introduced the fundamental concepts of PDAM. This chapter will expand the explanation of the integration of maintenance within PDAM.
The Core Maintenance Concept
Based on the principles of PDAM, the overriding purpose of a maintenance process is to support the goals and objectives of the senior management or asset owner. Many maintenance programs are purely equipment-focused.
That is, they are designed only around the requirements of the equipment without reference to the larger perspective of how the equipment’s performance can best support all of the organization’s objectives. This narrow focus often puts maintenance programs at a disadvantage when competing for limited resources.
Utilizing asset management principles, the most effective maintenance process would be based upon a core mission statement that could be linked to higher-level missions, for example:
The purpose of the maintenance process is to achieve the specified levels of asset performance at acceptable costs and risks and in compliance with all safety, health and environmental standards.
As required by PDAM, performance targets for asset reliability,
availability and service life should be tied directly to business goals.
Costs and compliance levels are established similarly. Within this framework, a PDAM focused maintenance process would have:
Work requirements based on established standards and criteria for
equipment condition or performance linked to the asset management
strategy
A screening process that ensures all performed work is justified and
necessary to support performance goals
A systematic decision process to evaluate and manage risks when
selecting maintenance tasks
Execution by the most cost-effective method
Measurement of benefit achieved
Performance metrics established, monitored and used as a basis for
continuous review and improvement of maintenance and asset
performance
The Maintenance Process
The only justification for an asset owner or senior management to purchase, install and maintain an asset is to receive benefits from the services the asset provides. This is as true for a circuit breaker as it is for a bucket truck. Of course, the services may differ, e.g.
contribute to safe and effective operation of the power system for the circuit breaker, but all service levels and their contribution to the organization’s goals should be clearly specified, understood and monitored.
The only justification for maintenance is to preserve an asset’s service level. Therefore, there should be a direct link between the desired performance level and the maintenance objectives. Assets are maintained in the O&M stage of their life cycle. Therefore, maintenance plans can be part of an asset’s life cycle plan.
Before exploring the maintenance process, the PDAM process needs to be understood in more detail than previously described. As shown in Figure
3-1, assets inventory and the services and performance they provide represent the final output of all the processes of the power delivery organization. These terms are used in the broadest sense, and there are two distinct but related aspects to asset/service performance. The organization’s assets are used to provide services to customers – end user service levels, often referred to as system performance in power delivery – and these can be measured by various metrics. Examples for power delivery include SAIFI, SAIDI, and energy-not-delivered and these are developed in the Performance Criteria process.(Bold text refers to specific blocks of Figure 1.) In addition, individual assets or groups of assets are expected to perform at certain levels, which may or may not affect end user service levels. Examples here include equipment availability, failure rates, return on investment, and maintenance costs. The service levels provided with the assets directly impact customers and are directly influenced by Asset condition and operating procedures. A later Chapter will explore performance metrics in more detail.
The Goals and Policies of the Stakeholders are used to develop criteria for decision-making and measurements of asset and system performance towards achieving those goals. To accomplish this,
performance indicators, specific qualitative or quantitative measures that allow performance against a benchmark to be assessed, are required.
This step involves transforming the high-level strategy into a set of decision criteria for evaluating actions at the lower levels. These criteria could be numerical, such as average restoration time requirements, or they could be qualitative, such as a decision that a safety related task has the highest priority. They range from internal business and engineering indicators to customer and financial perspective indicators. Whatever form they take, the criteria and performance requirements should be constructed to allow the asset and maintenance managers to use a mechanism for prioritizing activities that is consistent with the high-level strategy, goals, and policies of the asset owner. An associated step (not shown), after establishing performance criteria in this process, is risk identification and an assessment. The impacts from requirements for regulatory compliance are also included here.
The **Evaluate (**Asset Condition and System Performance) process takes inputs from the Identify Gaps process and either directly or with added calculations compares them with desired levels from Performance Criteria. Gaps or anomalies in performance levels may be identified here. The major output is a series of values that reflect the condition assessment of the system, groups of assets (fleets), individual assets,
or asset components. Examples are outage numbers and durations, number of equipment failures, and cost totals for a specific number and kind of task. This process addresses directly measured performance metrics and values with the objective of determining current asset condition or performance level.
Evaluations of asset and system performance provide data and quantitative information about performance of the Assets in meeting established Performance Requirements and information to predict their ability to meet these requirements in the future.
Performance monitoring, both directly and indirectly, provides the basis for management of an asset throughout its life cycle and indicates the need for changes to processes or procedures to ensure that program performance goals are met. Effective asset condition assessment in relation to its service performance is needed to understand the deterioration that leads to reduced asset performance in the future.
Evaluations of system performance also may be conducted externally
(e.g., customer opinions of infrastructure condition, or regulator assessment of the provision of services), and these are valid inputs for analysis as well.
The maintenance process is included within the Implement process depicted in Figure 1 along with all of the other tactical operations of PDAM. The initial step in developing a maintenance program is to develop maintenance strategies or plans. This process develops maintenance strategies and practices that are to be generally applied in the organization. Based on review and analysis of risk definitions performance criteria and risk assessments, industry practices,
manufacturer recommendations and historical data, this process’s outputs include maintenance procedures, value and timing of preventive maintenance tasks, condition-based maintenance triggers, maximum operating levels and other similar standards. Maintenance may be covered in the life cycle plans for specific assets or groups of assets.
Of course, maintenance currently is a well-established process in all power delivery organizations. In fact, some confuse good maintenance practices with asset management. Maintenance is an integral part of asset management, but only a part. The objective of an integrated asset management maintenance program is not to simply maintain equipment to some standard but to make certain that maintenance practices are developed in alignment with the organization’s goals and objectives.
Furthermore, assets are maintained to provide performance levels determined by an evaluation of the required benefits and risk tolerances and an evaluation of alternative actions.
The purpose of the next section is to describe how an existing maintenance organization may be integrated into the asset management approach. An analysis of these maintenance processes will show that they are simply a special case of the larger PDAM model presented in the preceding chapter.
Maintenance Process Diagram
A more detailed view of the maintenance process in an asset management context is shown in Figure 1. Bold text refers to specific blocks in that diagram.
Figure 4-1: A PDAM maintenance process (Source: Guidelines for Power Delivery Asset Management)
The first step in the process is to Identify Important Functions of the power delivery assets that are to be maintained. In addition to the expected equipment design specifications, this process should also consider the risk definitions and performance criteria developed from the senior management or asset owner’s goals, i.e., the asset management strategy. This process aligns the asset’s performance with the performance level required for the system of which it is a part. This means that identical pieces of equipment could have different performance requirements (e.g. reliability, availability) depending on where they are located in the power delivery system. These different performance requirements could translate into different maintenance requirements.
Once there is a quantification of the performance requirements for an asset, maintenance tasks and their frequency can be specified to achieve the desired results in the Develop Maintenance Test and Task and Frequency process. Note that these linked processes broaden the maintenance task selection from an equipment decision alone to one integrated into the asset management framework. The next step is
Develop Maintenance Program. Since maintenance budgets are limited to some level, there may not be funds for all identified tasks and tests. A program optimization process, similar to that described in Figure 1for the overall PDAM implementation, should be used to ensure that maximum benefits are being received from the maintenance program.
Perform Maintenance/Test is the next step, and it is important that the outputs of this process are available to the overall asset management process. These outputs include the relevant data from equipment history for completed maintenance activities, completed post maintenance test activities, predictive maintenance results, equipment condition data from completed PM, operator rounds, inspections, and any other sources of performance data. These data are a part of the maintenance process, but they also have uses in developing performance and condition trends, developing aging models for predicting future performance levels, assessing whether past investments have had the desired results and a number of other key PDAM activities. Therefore,
there should be a data link to make certain that information that comes from maintenance is available for other uses. One means to facilitate this is to have maintenance and test data reside in the asset information repository, rather than some separate maintenance database.
It is also probable that information from non-maintenance activities
(e.g. forced outages) can be of interest in the maintenance process and there should be a means to link and transmit this information.
Data from both sources should be evaluated to determine whether there is asset degradation. This determination is essentially a subset of the application of the previously described assessment algorithms for maintenance and test data. Here the process is usually much simplified and is often just a comparison of measurement results against asset specifications, e.g. power factor. An investigation of the asset deterioration may show that a change of maintenance tasks or frequencies would be warranted. If not, then another option may need to be developed
(e.g. change in supplier or operating practices). In any case,
corrective maintenance will be performed.
Equipment Failure is included here as a separate case and is defined as a failure of an asset to provide one or more of its defined functions. Philosophically, a failure could be considered as an extreme case of underperformance and with such a definition the general PDAM model would accommodate failures. In reality, most power delivery organizations treat failures as special cases. The first determination to make is whether or not the failure was preventable. For all cases except run-to-failure equipment, some effort commensurate with the equipment value should be made to understand the failure cause. It is recognized, however, that not all equipment failures justify a complete root cause investigation. If no cause can be determined, then the only course is to perform corrective maintenance or replacement and continue.
Even in these cases, good failure history data should be recorded for possible future analyses, such as developing equipment hazard rate curves, and trend identification. Good failure records will also help to uncover generic problems with specific designs or applications.
If the failure was found to be preventable, then the next series of questions are designed to determine whether the failure was maintenance or operations related. If indicated, changes to the procedures or maintenance tasks should be investigated. If no other cause has been identified and the risk of another failure exceeds an acceptable level,
then a design change should be investigated.
PDAM Maintenance Characteristics
Outlining the development of a complete maintenance process for a power delivery organization is well beyond the scope of this document and, in fact, unnecessary. Maintenance is a well-established process in power delivery organizations and significant expertise has been acquired for keeping power delivery equipment in operating condition. On the other hand, maintenance departments are less adept at explaining how to value their contributions to the organization’s performance. PDAM can provide both a basis for demonstrating the relative importance of maintenance and a means to improve the effectiveness of existing maintenance programs.
Given the opportunity to build a maintenance program from the ground up one would begin by answering a series of questions about each asset starting with:
What are the required functions for this asset?
What is the required reliability?
What is the required availability?
What is the acceptable service life?
What is the acceptable level of risk resulting from under
performance?
What is the acceptable average annual maintenance cost?
How does each particular maintenance task affect the asset’s
performance and how is the effect measured?
Unfortunately, generally there are few analytical answers to these questions available for power delivery equipment. Rather, the response a maintenance manager would most likely receive is something along the lines of: as high as possible, for as long as possible, for as little cost as possible. The direction would be clear, but it is difficult to make the necessary quantitative decisions without quantitative data.
Furthermore, it is difficult to justify maintenance budgets or defend against cuts without an analytical basis. Asset management can provide a framework to address the lack of data and begin to provide a quantitative basis for maintenance decisions. Some key steps are:
Understand the operational policies and the service levels
demanded of the asset to fulfill the service and policy requirements
of the organization as set by the asset management strategy. Power
delivery equipment usually is not specified in terms of operational
reliability. Industry standards may specify some design
requirements, e.g. number of operations for a circuit breaker, but
there generally is no quantification of equipment reliability or
availability. Similarly, system and substation designs may be chosen
based on computer simulations that calculate an expected service
level using nominal component failure rates but there is no link
between the design values assumed and the components eventually
installed. For older equipment, there is usually some degree of
condition degradation but any effect on equipment performance, let
alone the station or system, is difficult to quantify.
Perform a risk assessment of each asset. Maintenance resources
are limited, and their use should be directed to the assets that
most contribute to performance goals, especially risk levels.
Another way of stating this is that maintenance should be
prioritized to address those assets whose lack of performance has
the greatest impact on system performance goals or risk exposure.
(Risk is more fully discussed in Chapter 6.) What may occur as a
result of a failure is directly related to where and how the
equipment is used in the power system. A measure of the equipment’s
application failure consequences may also be referred to as the
equipment’s criticality by some. Formally, risk can be defined as
the product combination of the probability of a hazard causing loss
occurring and the consequences of that loss. In the context of PDAM,
the hazard of most interest is the failure of the equipment and, as
discussed above, ascertaining the probability of equipment failure
is difficult. EPRI has a number of research efforts underway to
develop select power delivery equipment failure hazard rates. (See
epri.com for details). Obtaining some qualitative measure of
consequences may be easier. It is possible to determine the
qualitative potential for significant failure and consequences -
financial, environmental, safety and other adverse impacts on
performance goals and rank assets or systems in order of their
potential impact to the organization.
Define maintenance requirements based on the equipment’s risk
contribution analysis. This will establish the most cost-effective
maintenance approach for each asset based on the asset’s
contribution to system performance.
Select the best maintenance strategy for each asset. Use the
principles of RCM or similar analytics-based decision processes to
choose condition-based tasks, time-based tasks or corrective tasks.
For defining maintenance requirements and selecting maintenance tasks,
consideration should be given to the characteristics of the equipment but also to the specifics of each application and the corresponding operational and environmental stresses. Identical types of equipment may require different types of maintenance due to different applications.
PDAM Maintenance Implementation
Maintenance is often organized on three levels:
Maintenance plans are directed at specific equipment types, makes
and models and are composed of tasks specific to that equipment.
These may be included in some asset life cycle plans.
Maintenance programs combine sets of maintenance plans and are used
to organize and manage maintenance resources.
Maintenance strategies link maintenance programs to the
organization’s goals and objectives.
Maintenance programs generally are constrained by resource limitations,
primarily economic. On the other hand, there are two main drivers for maintenance:
Safety. The safety of the public and utility personnel is the
primary consideration in any maintenance program. Safety involves
maintaining equipment and systems so that they can be operated
safely under any foreseeable circumstances.
Service Reliability. Reliability of service usually is measured
by the frequency and duration of customer service or load delivery
interruptions. The impacts of maintenance on interruptions, both
planned and unplanned, and service reliability should be analyzed.
Ideally, to address these drivers effectively while working within the constraints, a maintenance process, which crosses all three levels,
should:
Plan equipment maintenance taking into account how maintenance
interacts with other activities and events affecting service
performance
Be based on equipment reliability and availability goals that
balance costs with achievable circuit performance, life cycle
management and service requirements.
Direct resources towards maintenance work that will best support
meeting the organization’s goals and strategies.
In reviewing a maintenance program, some questions to be considered are:
What is to be accomplished?
Short-term goals
Long-term goals
How is progress measured?
Metrics
KPIs
What information is needed?
For goals
For measurements
What is available?
Data
Analysis tools
Decision support tools
How to fill any gaps?
When decisions are made about the level of reliability, or performance metrics, to be achieved in a system, attention must be paid to the economic factors, such as the incremental costs of reliability, the benefits expected from a change in reliability, and the allocation of the reliability investment among the system components.
PDAM Fleet Management
One of the most pressing needs for new and better tools to support power delivery maintenance, including replacement, is in the area of aging asset management. Even for those utilities not faced with a large aging asset problem, quality long-range planning requires a good understanding of the current and projected future condition of the asset base.
It is well accepted that the risk of power delivery equipment condition deterioration and wear-out failures increase as equipment approaches the end of its useful service life. The rationale and timing of increased maintenance or investment decisions in anticipation of this increased risk have been traditionally left to historic patterns and engineering judgment. This may result in higher costs to the utility and its customers if investments are not optimally timed, i.e. if made too early, they may result in higher carrying costs; if made too late they may result in reduced service and higher failure costs. PDAM with an aging asset infrastructure makes it increasingly critical to identify equipment hazard functions of major asset classes and to understand the risks and influence of critical variables on equipment failure rates
(failure in this context is defined as a functional failure that necessitates asset replacement). With better failure rate information,
risk-based models of the underlying distribution of failures as a function of age (hazard rate or “bathtub” curve) of each asset type can be developed to allow optimizing the risk-cost function in the maintenance planning and decision-making processes.
Figure 4-2: A hypothetical equipment age distribution and failure rate curve
The general situation is depicted in Figure 4-2. An example histogram showing the number of units of a particular equipment type in several age brackets has been superimposed on a failure rate curve that shows an increasing failure rate with age for that type. The age profile of the units reflects previous investment patterns. As time progresses, an increasingly larger percentage of the equipment population will move into the range of higher failure rates. This type of histogram gives rise to the term “asset walls.” A significant concentration of a particular asset in a group of adjoining age brackets looks similar to a wall moving forward in time.
As illustrated in Figure 4-2, the assumption of constant failure rate only applies if the equipment in question is operating in the flat portion of the hazard rate curve (if it exists). Furthermore, many factors can influence equipment performance causing variations in equipment failure rates with time and usage. These could include equipment age, loading, manufacturer and maintenance history. A better understanding of how these additional stress factors affect equipment performance is required to correlate the relationship of various parameters with the probability distribution of equipment time to failure and failure rate.
Approaching asset walls and new operating requirements make projections based on only historical failure counts increasingly risky. Planning maintenance, estimating future capital requirements and making the best effective capital funding decisions in the present requires a better understanding of asset performance projections over the long term.
Understanding individual asset performance is important for tactical planning but understanding the collective performance for groups of asset types, often called fleets, is important for PDAM, especially where maintenance programs are not tailored to individual units.
Linking Maintenance to Equipment Reliability
Business pressures to contain costs while preserving service standards,
including the use of performance-based rates, have highlighted the lack of understanding of a quantifiable relationship between maintenance and equipment reliability and other performance metrics. Intuitively,
maintenance should improve equipment reliability and consequently system performance, but it is difficult to quantify any linked improvement.
However, equipment failure rate models are needed for power delivery organizations to plan, operate and maintain their systems at the required levels of reliability for the lowest possible cost. Managers would like to know what the increase in reliability would be for an incremental increase in maintenance funding or conversely, what is the impact of a reduction in maintenance on system performance.
Performance Measurement
The recurring theme throughout all of the above discussion is performance measurement. Good performance metrics are important for PDAM and effective maintenance. Properly established maintenance performance metrics provide:
A basis for establishing maintenance objectives.
A scale to measure maintenance effectiveness
Insight into asset performance issues
The principles of asset management require that all are linked to the organization’s operating goals and asset management strategies. The next chapter will discuss performance measures in more detail.
1.5 - Chapter 5 - Performance Measures
In a PDAM approach, decisions to allocate resources are based on policy goals and objectives and the resources required to obtain those results.
This includes both maintenance and replacement decisions.
Performance measures enable organizations to translate high-level policy objectives such as service reliability into quantifiable expressions of results to be achieved. They provide information by which asset managers can make tradeoffs across competing needs and measure the effectiveness of results. There are three broad classifications for PDAM performance metrics presented:
Measures
Performance Measures
Key Performance Indicators
Background Definitions
Before proceeding, it will be helpful to review some definitions previously presented:
Goal - a broad statement of the long-term results needed to accomplish the organization’s mission and achieve its vision. Strategic goals are broad statements defining changes the organization hopes to achieve during the strategic planning horizon. Goals focus on outcomes or results and are qualitative in nature. Goals are defined as broad,
ideal future conditions, the results the organization wants to accomplish. As an example, one of the possible goals for distribution could be:
Reduce the number and duration of customer outages in every operating region.
Strategy - A strategy explains the way a goal or group of goals will be achieved. Strategies are statements of major approaches or methods for attaining each goal and resolving specific issues. An example strategy for the above goal is:
Improve the reliability of the ten worst performing circuits in each service area.
Objective- Strategic objectives flow directly from strategic goals.
They translate the quantitative expectations of goals into specific and measurable targets that the organization should meet to realize the goals. Objectives tell what the organization plans to achieve, who will do it, how to know when it is achieved, and what are the key performance indicators for tracking progress. Unlike goals, all objectives are expected to be met within the planning horizon. As an example, an objective to the above-stated goal to provide the best distribution reliability service in the area could be:
Have no feeder with a SAIFI greater than x by y year.
Some additional definitions that will be useful in the following discussion:
Input – the amount of resources used to conduct an activity
Output – product or service; work completed
Outcome – the results achieved by the output (e.g. customer
satisfaction)
Target - the specific level of performance the organization is
striving to achieve
Measure - any meaningful indicator used to determine the magnitude
or degree of something.
It is important to keep in mind that not every measure relates to performance or is suitable to use as a key performance indicator.
Performance Metrics
Performance metrics are quantifiable and observable measures that support process and project objectives. They help guide progress to achieving those objectives. Performance targets are the specific levels of performance expected to be achieved. This target may be established for a particular time period and level of funding. It provides the basis for comparing actual performance data. SAIFI, for example, is not only a natural unit for measuring service value but also a way to measure performance, i.e. was the target met. For a transmission system, the number of line outages has similar interpretations.
There are, in fact, two general classifications of performance metrics of interest to PDAM. Results metrics measure what has been accomplished.
Process metrics measure how the results were achieved. One conceptual approach to developing a list of performance metrics is to use a process model formulation. Performance criteria defined in the development of the organization’s goals can be considered as the outputs of various processes and can be measured. The inputs to the process that produce the result are measured by process metrics. Going back to the SAIFI example, the index is the result of a process with many inputs that can be measured such as vegetation management and equipment maintenance,
each of which can be considered as a sub-process with its own process metrics (e.g. maintenance backlog).
Defining and measuring performance metrics of both classifications is important for good asset management. It is important not only to direct resources properly to maximize value but also to utilize the resources efficiently and effectively in attaining that value. In addition, good asset managers want to respond to deviations in results metrics quickly.
To do this, it is important to know what influences the result metric.
Process metrics provide this information. Furthermore, most results metrics are lagging indicators. This is to be expected since they are the output of a process. A distribution manager only knows that the SAIFI metric has gone below target or has not been reached after the fact. Process metrics can be leading indicators (e.g. maintenance backlog). Tracking and managing them can improve the performance of the result measure.
For power delivery, there are usually many inputs to a process that has a single result, and one should be cautious about developing a performance measure for each. The potential benefit should outweigh the cost of data collection and storage, and it makes no sense to gather information that will not be acted upon.
In essence, a performance measure is appropriate for asset management if it supports better decision making about resource investment, and if the organization’s actions could influence the value of the measure. Some additional issues to be considered:
Does the measure relate to strategic objectives?
Is the measure important to the affected stakeholders?
Given available resources, can the measure be accurately and
reliably determined?
Can the measure’s value be reasonably predicted for different
scenarios?
Can output or program delivery measures be used to indicate early
progress towards desired outcomes, and related budgets and
performance targets?
Are procedures in place to collect performance information
efficiently and accurately, and to communicate results in a useful
and usable manner to the intended audiences?
Guidelines for Developing a PDAM Measure
The selection of a performance measure is based upon attributes of the related objectives and goals. In other words, it is a direct measure of some attribute. Some considerations for deciding which measure(s) should be used to track progress in attaining a goal or objective:
Determine the desired result associated with the goal or objective
Examine the types of measures: input, output, or outcome. Is the
goal or objective associated with operating efficiently
(input/output), producing something (output—amount of work done); is
it about the quality or timeliness of work (outcome?)
Consider how the measure will be used apart from monitoring
progress. For example, to verify compliance with a mandate,
benchmark against other organizations, or to compare different
groups within the organization?
Who will use the measure?
What specific data is required to measure progress toward or
attainment of the goal or objective?
Key Performance Indicators
Key performance indicators (KPIs) are, in a general sense, the same as the performance metrics discussed above. However, the term has come to be associated with metrics that are tracked and reported to a higher level of authority. For this reason, KPIs usually focus on measuring accomplishments or results. The commonly used reliability indices are an example. They directly measure a dimension of customer service level that is the result of many factors – design, maintenance, capital investments, etc. Trigger parameters in performance-based rates can be considered as KPIs and they too are the result of many separate activities.
Key Performance Indicators are quantifiable measurements, agreed to beforehand, that relate to the critical success factors of an organization as documented in the asset management strategy. They will differ depending on the organization. Whatever KPIs are selected, they must reflect the organization’s goals, they must be meaningful to its success, and they must be quantifiable. Key Performance Indicators usually, but not exclusively, are long-term considerations. The definition of what they are and how they are measured do not change often. The goals for a particular Key Performance Indicator may change as the organization’s goals change, however.
Key Performance Indicators help an organization define, measure and communicate progress toward organizational goals. Because so many factors can influence a high level KPI, it can be difficult to gain insight when some corrective action is required. For this reason, many organizations develop lower level KPI, for example: maintenance metrics such as number of backlog maintenance orders. In fact, KPIs can be useful at any level of the organization if they are properly chosen.
Good KPIs should:
Focus on accomplishments, outputs or outcomes, rather than
activities
Utilize readily available data
Provide meaningful indication of performance to the interested
levels of the organization
Allow external comparison (benchmarking)
Communicate progress
Distribution Customer Service Performance Measures
In the power delivery industry environment, customer service has become a critical issue while cost control remains just as important an element. As customers expect better service at lower cost, regulators are becoming more concerned about service reliability. The number of regulators enforcing distribution reliability standards continues to increase with more rate decisions tied to service reliability. (The application of transmission level performance standards is increasing and the key concepts presented here about distribution system performance measures are applicable to transmission.) The challenge for utility engineers and managers is deciding how resources should be spent on reliability to provide the customer the service required, at a price that satisfies all stakeholders. Utilities need to establish reliability and power quality as a core business strategy and take a longer-term view of performance improvement. Being a successful distribution utility requires focused attention on reliability and power quality. Once reliability and power quality have been established as a priority business strategy, performance objectives need to be established.
Objectives should be based on performance, not activity. For example,
the frequency and width of right-of-way clearing is less important than the effectiveness of vegetation management operations as reflected by system reliability.
Customer service is of primary importance to any distribution organization and a number of indices have been established. Load point indices measure the expected number of outages and their duration for individual customers. System indices such as System Average Interruption Duration Index (SAIDI) and System Average Interruption Frequency Index
(SAIFI) measure the overall reliability of the system. These indices can be used to compare the effects of various design and maintenance strategies on system performance.
A key issue with reliability metrics is the debate about comparing these indices from one geographic area to another and exactly how the input data should be applied in making the calculations. In addition, there are concerns about how to “normalize” the indices for adverse weather.
Many state public utility commissions require utilities to compute and track certain reliability indices, but comparing them from region to region and utility to utility has been problematic due to differences in how the data is applied, system designs, weather differences, and operating requirements. Because of this, the indices are limited in their usefulness for such purposes. If the calculation method is kept the same, they are useful within a specific geographic area in evaluating changes in reliability over time and as a measurement of the effectiveness of maintenance practices. Nonetheless, these indices are well established and several are listed below:
Sustained Interruption Indices
SAIFI – System average interruption frequency index
SAIDI – System average interruption duration index
CAIDI – Customer average interruption duration index
Momentary Interruptions
MAIFI – momentary average interruption frequency index
Load Based Interruption Indices
ASIFI – Average System Interruption Frequency Index
ASIDI – Average System Interruption Duration Index
Due to their widespread use, there is no need to discuss these indices further here. Suffice it to say that these could all be considered KPIs.
No parallel indices have been established for transmission assets.
Some example KPIs used by transmission and distribution power delivery organizations are listed below. This list is not intended to be exhaustive but rather to show the variety of possible indicators that can be tracked. The KPIs are grouped by reliability, safety, work processes, cost, equipment, and financial.
Reliability
ASAI
CAIFI
CAIDI
LAIFI
MAIFI
SAIFI
SAIDI
Number of events without switching remedy
Failures without switching remedy / Failures total
Number of customers impacted by outage
Number of customers impacted by distribution bus outage events
Correct performance of protective equipment per event
Number of distribution bus outage events
Load not served
Vegetation caused SAIFI
Vegetation interruptions per 100 miles
Distribution system vegetation management non-storm SAIFI contribution
Safety
Number of human errors events
Man-hours without injury
Lost workday case incident report
OSHA reportable incident rate: by department
Responsible vehicle accidents: by department
Safety required training: by department
Work Processes
O&M costs per bay by voltage
% Critical maintenance complete
Restoration time
Maintenance backlog
PM hours/emergency hours
CM work orders/ PM work orders
CM resulting from PM inspections
Non-weather overtime as a % of regular time: by department
Overtime hours by reason
Overtime hours by reason as a %
Distribution system routine trimming program scheduled vs. completed miles
Jobs started on time: by department
Work completed on time: by department
Event investigation performance: by department
Mapping & document services: work backlog
Planned/completed as scheduled
Costs
Cost of maintenance
By equipment group
By equipment model / manufacturer
By area / region
By maintenance category
PM/CM cost ratio
O&M costs / MWh delivered
O&M costs / MW installed
Call Center Costs average / customer
Distribution system trimming program cost per mile
O&M cost/customer
Capital costs/customer
On hand inventory $/system Mw installed
On hand inventory $/customer
Planned O&M cost/emergency O&M cost
O&M cost/ Mw installed
O&M cost/ MWh delivered
Capital cost/ Mw expanded
Capital cost/ customer connected
Total budget performance by department
O & M budget performance by department
Capital budget performance by department
Spending on consumables, small tools and material: by department
SF6 Gas Use – replacement pounds per month
Equipment
Average equipment age
% Tasks requiring custom-built parts
Average equipment utilization factor
Failure rate by equipment group
Mean Time Between Failures - by equipment types, service areas
Mean Time To Repair - by equipment types, service areas
Substation SAIDI
Substation SAIFI
Pole replacement – miles inspected
Financial
Capacity investments/Capacity installed \[$/MW\]
Capital investment/Total book value
Book value / MW installed
Book value / MWh delivered
Inventory turnovers
Inventory costs/MW installed
Inventory carrying charge
PDAM and Performance Measures
PDAM can help to identify performance measures to support resource allocation decisions regarding operation and maintenance. PDAM can also help to develop a framework for selecting appropriate performance measures and establishing performance targets.
For this guide, rather than attempting to recommend one set of performance measures suitable for any power delivery organization, it is more productive to define criteria for a useful performance measure in an asset management context. Although the selection of particular performance measures can influence what gets done, equal attention should be directed at the various ways performance measures are used and incorporated into maintenance processes and other asset management decisions. Utilities also face challenges in making the best use of available resources. However, there are significant differences across companies regarding service territory, organizational structure, policy objectives, system design, management processes, staff capabilities,
data and performance measures already in place. Each of these factors has a bearing on the performance measurements and approaches that will be most effective in a particular situation.
Without question, the paramount performance measures for distribution systems are the well-established reliability indices, SAIFI and SAIDI.
Although the details of their calculation may differ, most distribution organizations track performance in these terms. More recently, several states have incorporated reliability indices into their regulatory structure and linked them to rates. Such performance-based rate mechanisms make it easier to monetize the value of a reliability index but, as previously discussed, these indices are output measures. They are the result of the combination and interactions among a large number of variables. Asset and maintenance managers only can affect these output performance indicators by changing one or more of the controllable input variables.
The problem is that the relationship between changes in the inputs and the outputs are not well quantified. For example, it is clear that customer reliability can be influenced by substation equipment reliability, and it is possible to determine how many customers would be impacted by a particular equipment failure by examining system topology and layout. The problem comes when trying to determine how equipment maintenance can improve a customer reliability index. What would be the corresponding improvement in customer reliability for increased equipment maintenance? Without quantifiable relationships, it is difficult to direct resources most effectively. This is a focus of ongoing industry discussion and research.
Secondary KPIs
The concept of secondary KPIs is illustrated by the following example.
Figure 1 shows how both power quality and the number of interruptions influence a higher-level goal, reliable customer service. Forced interruptions are tracked by a number of previously reviewed reliability KPIs. (The KPIs used are for distribution but the general concept applies equally well to transmission.) Managing these indices is an important responsibility for system operators but, as discussed earlier in this chapter, these are lagging, or output, KPIs. They only indicate a deviation from the desired performance after it has occurred. To better anticipate and control possible shortfalls in performance, one must identify and track the process inputs, the leading indicators. For the case illustrated in the figure for example, some input factors to the maintenance process that could affect outage duration include:
Crew productivity
The crew assignment process
Crew skills and knowledge
Availability of replacement parts
Figure 5-1: The relationship of primary KPIs to high level goals
Of course, there are many other factors and these four are often tracked as internal processes within a maintenance organization. However, their impact on higher-level goals such as customer satisfaction often is not considered or quantified. Without an explicit link, it is more difficult to justify reallocation of resources or manage proactively.
PDAM implementation should start by examining all the factors that influence a performance goal, then developing a relationship measure and finally setting performance targets. An illustrative but simplified example of the process might start with the question: What determines customer service reliability?
The important factors are:
System
Operations
Design
Equipment
Design
Condition
Maintenance
Efficiency
Effectiveness
Focusing on maintenance, what are some secondary maintenance KPIs that can be tied to reliability? A partial list would include such measures as:
Preventive maintenance backlog
PM/CM cost ratio
Vegetation incidents by line
PM by equipment
Equipment condition index
Age
Diagnostic tests results
Time since last overhaul
The next step in the process would be to develop a set of management metrics to measure performance that would combine a number of secondary KPIs in order to provide a broad-based leading indicator. The details of the process would depend on the organization’s higher-level performance goals and metrics. Examples include:
Corporate objectives or mission statement
Balanced score card
Incentive programs
Performance-based rates
Links to the next level of KPIs would be established and then the secondary KPIs would be identified. The types and quality of data available would drive this process. Depending on the particular activity, tertiary KPIs may be established. Once the linkages were determined, influence models could be developed to quantify the relationships among the process inputs and the final output, the higher-level goals. The entire exercise would start with simple models using available information expand with experience. The models would:
Show where the organization is now
Quantify and track changes in higher level KPIs as changes are
implemented in lower-level processes
Compare areas, stations, equipment types to highlight good and bad
actors
Provide analytical support for changes in resource allocation
Give people meaningful targets to which to manage
1.6 - Chapter 6 - PDAM and Risk
Background
The term “risk” has appeared often in the preceding chapters. This chapter will provide an expanded discussion of risk and risk assessment and management.
In the broadest sense, risk can be considered as a measure of the uncertainty of business performance and as such is closely tied to the organization’s performance goals. The subject of risk has been extensively studied for financial assets and is generally considered to be the risk of not achieving the expected financial return. Risk has also been studied in detail for the power generation and energy trading side of the utility business. Here the desire is to assess energy portfolio exposures to commodity markets and customer loads, evaluate overall portfolio risk in terms of cash-flow-at-risk or value-at-risk,
and assist in designing portfolio risk management programs. For power delivery, risk management is not so well formalized.
Utilities must manage an array of potentially conflicting business objectives, including maintaining economic performance, improving customer satisfaction, providing high reliability, addressing regulatory uncertainty, and complying with increased environmental regulation. The result is that many utilities are considering or have moved towards implementing asset management concepts and decision-making procedures based on minimizing equipment life-cycle cost and risks.
As discussed earlier, Power Delivery Asset Management (PDAM) begins with the fundamental premise that all asset management decisions made by utilities should contribute to stakeholder values, as set forth in the organization’s goals and policies [1]. PDAM applies this premise in decision processes at every level of the organization. The resulting alignment of decisions with criteria and value measures derived from the asset owner’s or senior management’s direction helps ensure that asset management decisions consistently support the organization’s strategic objectives, performance goals and risk tolerances.
As a consequence, risk assessment and management are important elements in any well-developed asset management plan. A growing number of utility managers are applying resources to improve their understanding of and capability to make risk-based decisions. Therefore, there is increasing interest in the methodologies and tools needed to more accurately assess equipment performance and risk and to provide quantitative information to support asset management and operations decisions.
Definitions
Before proceeding further, it will be helpful to establish some definitions for the terms used in this report.
Risk can be defined as a measure of the probability and severity of adverse effects [2].
There are two major classifications of power delivery risk:
Technical risk
Programmatic risk
Technical risks are usually associated with the performance of the power delivery equipment and system, including software of computer-based devices. Common examples are the risk of not meeting a reliability target due to excessive equipment failures or the risk of spending more than budgeted for maintenance due to more maintenance activity than anticipated.
Programmatic risks are usually associated with new construction or replacement projects and would include cost overruns or schedule delays.
Of course, programmatic risks exist in normal power delivery operations and maintenance activities also. The maintenance budget overrun mentioned above, instead of being due to poor equipment performance,
could also be due to poor or incomplete budgeting or work practices.
Adverse effects can result from many factors for complex,
technology-based and geographically dispersed organizations such as electric utilities. Power delivery managers must contend with risks that come from a wide variety of sources such as:
Weather and other natural causes
Human errors
Technological failures
Equipment
Software
Financial
Political
Cyber and physical attacks
The objective of the work described here is to investigate tools for assessing performance and risk for power delivery equipment. Therefore,
the focus of the remaining discussion will be on the technical risks associated with the operation of such equipment. This is defined as
operational risk and is primarily concerned with the potential failure of the equipment to perform one or more of its intended functions as expected. Examples include premature failure or lower than specified availability. The terms risk and operational risk will be used interchangeably for the remainder of this report.
A typical case might relate to the assessment of the risk associated with deferring investment in a new transformer. In this case the business risk per year is the sum of the possible consequential costs of failure, the expected capital cost and the maintenance cost per year.
The expected consequential cost is the product of the consequential cost if failure occurs times the probability that a failure occurs.
Similarly, the expected capital cost is the product of the probability of failure times the inflated capital cost in the year that failure might occur. Another example would be the assessment of the risk of premature failure from increasing a transformer’s loading in order to meet some system operating requirement.
Risk assessment is the attempt to answer the following questions
What can go wrong?
What is the likelihood of that occurrence?
What are the consequences?
Answers to these questions help managers identify, quantify, and evaluate risks and their consequences. Power delivery equipment performance, and therefore the equipment’s operational risk, is determined by a number of factors. Among them are:
Design
Construction
Installation
Application
Operating environment
Maintenance
Because of the complex interactions of these factors, the assessment of equipment operational risk requires a high level of equipment expertise.
The loss of an important asset, say a tie line power transformer, will adversely affect one or more business performance measures (e.g.
equipment availability, wheeling revenue). Assessing this risk requires evaluating the potential for a shortfall in the affected performance measures and quantifying the probability of transformer failure.
Identifying the various ways in which these losses may occur and their likelihood are tasks for transformers experts.
The following sections will discuss the risk assessment process. Because of the great variety of equipment and operational risks encountered in the power delivery industry and the wide variation in utility business practices, this discussion will be focused at the conceptual level. The objective is to provide a foundation that can be utilized to develop more detailed asset management tools in a unified and consistent manner.
Risk management builds on the risk assessment process by seeking answers to a second set of questions [4]:
What are the options?
What are the potential costs, benefits, and risk of options
identified?
What are the impacts of these options on future options?
The six primary steps in risk management are:
Identification
Assessment
Analysis
Mitigation
Allocation
Tracking and monitoring
The details of each step will depend on the type of risk being managed.
The purpose of risk management is to minimize risks to acceptable levels, proportional to accomplishing the organization’s related goals.
Consequently, risk management utilizes and depends upon the outputs of the risk assessment tools. Asset management principles dictate that risk management must be an integral part of the overall management process as set by the asset management strategy.
Risk Assessment Requirements
Perhaps the most significant power delivery equipment operational risk is the unexpected failure of equipment. (Although most focus on the cessation of equipment operation, failure in the context of risk assessment is more generally the failure to meet any performance threshold derived from the organization’s goals and objectives. For example, equipment availability is affected by maintenance outages as well as forced outages resulting from failure.) Operational risk, in mathematical terms, is the product of the probability of the occurrence of the failure event times the cost of the failure consequences.
Mathematicians call the failure rate curve, commonly referred to as the bathtub curve, the hazard rate function. It gives the failure probability at each age. The hazard rate function defines the probability of surviving up to a certain age and then failing in exactly the next higher age bracket. If the general shape of the bathtub curve is assumed to be correct, then there are actually three distinct regions corresponding to different failure rates as shown in Figure 1.
It is clear that risk assessment implies a time dimension. In the long run, all equipment will fail. Therefore, part of the assessment process is the establishment of the time frame for the assessment and the proper utilization of any hazard rate function for that time frame. As an illustration, consider power transformers. When new, the risks of failure come primarily from a manufacturing or installation defect. The probability of a manufacturing defect related failure decreases over time. At any time of operation, failure may result from an external cause such a lightening. When the transformer is old, the risk is primarily that of wear-out. The familiar bathtub curve results from combining these three hazard functions over the transformer’s life.
The appropriate hazard rate function will be determined by the assessment time horizon. In addition, the consequences also are a function of the assessment time horizon and the particular hazard being assessed. The costs resulting from an unexpected early failure will likely be greater than those resulting from a wear-out failure, which can be better anticipated. However, quantitative data on hazard rate functions for power delivery equipment are sparse and providing such a curve valid for a particular individual unit is a major challenge for assessing power delivery equipment risk. EPRI efforts to address this need will be discussed later.
Figure 6-1: Failure rate bathtub curve
Assessing risk as the probability and severity of adverse effects requires knowledge of the hazards and threats to the equipment. These are multifaceted and can be represented only through multiple metrics.
Equipment’s susceptibility to future failure is a function of its current state and future stresses. Furthermore, the equipment state is dynamic and changes over time and in response to future operational stresses and maintenance.
The risk assessment process is not an end to itself but rather its results should be used to manage and mitigate risk. There are three fundamental approaches to equipment risk management. One is to control or improve the state of the equipment, for example by performing preventive maintenance. The second is to reduce the effects of the threat by actions that do not necessarily change the equipment’s state,
for example by installing lightning arresters. The third is to reduce the consequences, for example by maintaining adequate spares.
Determining the appropriate risk mitigation measures requires assessing the hypothetical risk and performance, including cost, of the candidate approaches.
Ideally it would be desirable to be able to translate all of the consequences of equipment failure into financial terms. Obviously some consequences, for example capital cost, repair costs and replacement costs, can be evaluated directly in financial terms based on experience and recent price and cost trends. Unfortunately, there are other adverse impacts on a utility caused by equipment failure, which may be very important; but which are difficult to quantify in dollars and cents.
Evaluating these consequences requires a set of business rules that will most likely vary from utility to utility.
Assessment Methodology Concepts
Because there are many different power delivery equipment types with fundamentally different designs and ratings from suppliers with different design philosophies and manufacturing techniques, simply applying any generic failure rate, even if available, to a specific unit is of dubious value. Any industry average generally reflects the performance of all units in the study, some of which may be of poor design, subject to heavy operational duty, or receive poor or infrequent maintenance. The proportion of troubled and high performing units in a comparison study is generally unknown. Any industry average also would include units of every age, from new to those approaching end of life.
Again, the proportion of each of these categories would be unknown.
There is another, even more basic problem, with applying the classic hazard rate curve to power delivery equipment. The wear-out portion of the hazard curve implies a direct relationship between age and deterioration. However, for major subsystems of critical power delivery components, wear-out is proportional to service duty and service duty is not always closely correlated with chronological age. For example,
circuit breakers’ mechanical linkages wear as a function of the number of operations and interrupter components deteriorate as a function of the accumulated interrupted current. For transformers, the aging rate of the insulation system is greatly influenced by oil temperature, oxygen and water content. Age alone is not necessarily sufficient to accurately place a unit on a hazard rate curve.
Depending on past operational stresses, the equipment’s current condition may not correspond to that which would be expected for its chronological age. A unit with a history of heavy loading may exhibit a condition more like that of an older unit. In effect its apparent, or effective, age would be greater than its chronological age. Information about past loading and other stresses experienced by most power delivery equipment is not commonly available for its complete past service life.
However, effects of past operation can be expected to be reflected in the equipment’s current condition. Therefore, for accurate risk assessment there must be some measure of the actual condition of the equipment so that the effective age can be determined.
Furthermore, power delivery equipment deteriorates through the accumulation of operational stresses and ultimately fails. Like all mechanical devices that are placed in service, the materials that are used in the equipment eventually weaken and deteriorate which in turn reduces the capability of the equipment to withstand future operational and environmental stresses. Consequently, future failure rates are also a function of future stresses. A unit that will be lightly loaded going forward will age at lower than the nominal rate. Therefore, some measure of future operational stress should be determined and utilized for performance and risk assessments going forward.
The preceding discussion provides the conceptual basis for a power delivery equipment risk and performance assessment methodology as shown in Figure 2. Ideally, assessing the probability of an adverse effect requires a nominal hazard rate and the means to modify the probability calculation to account for equipment effective age and the magnitude of future stresses. At this time, nominal hazard rates for many power delivery components are not well established. In these cases,
qualitative inferences must be made about the likelihood of future performance. Even in such cases, modifiers related to effective age and future stresses would be appropriate.
Conceptually, the approach is quite straightforward as presented. The complexity comes from the detailed equipment expertise that is required to apply it to produce meaningful and useful results.
Figure 6-2: Substation equipment risk and performance assessment methodology concept
End of Life
Ideally, utilities would wish to replace an asset just prior to its end of life, but just when does end of life occur? There are various definitions of the term “life” for every asset, including power delivery equipment. The definitions most commonly used in power delivery are:
Design life: The anticipated length of time for using the asset.
There is neither formal definition nor technical basis for design
life, but the general usage is that it is the expected age beyond
which either failure will occur, or the risk of failure will become
unacceptable. The concept arises from the fact that the original
equipment designers and purchasers did not expect the equipment to
be in service much beyond this length of time. This term is used in
an anticipatory sense, not as some date certain and generally is
applied to all of an entire class of assets, e.g. high voltage
SF6 circuit breakers.
Many devices remain in service beyond this age, and many do not.
Book or tax life: The lengths of time over which the assets will be
depreciated. The accounting rules applied to a particular asset
specify this life. This is simply a financial fiction, not
necessarily related to actual physical degradation of the asset’s
ability to function, and generally is applied to all of an entire
class of assets.
Physical life: The maximum physical life of an asset ends when its
repair is physically impossible. This life is individual asset
specific but has less significance for repairable assets that are
assemblies of replaceable components.
Economic life: The length of time during which keeping the asset is
economically justifiable. The economic life of an asset is defined
by the period of time that minimizes the net annual cost for the
investment when, as for most power delivery equipment, there is no
assigned income stream, and considerations primarily consist of
costs. The assumption is that, over some period, operation and
maintenance costs increase with time faster than other ownership
costs decrease and the economic life ends at the point where the
combination of the two costs is minimized. This life may be specific
to an individual asset or fleet of assets. In practice, it may be
difficult to determine due to the difficulty of accurately assessing
the associated costs.
Operational or risk-based life: The length of time during which the
asset is expected to operate with an acceptable risk of failure.
This life ends when the asset’s risk of failure exceeds the limit of
acceptable risk determined by the asset owner. This life may be
specific to an individual asset or fleet of assets. For example, a
power transformer may have reached its operational life when DGA
results exceed some thresholds. As another example, a utility may
have a policy to replace all high voltage breakers at age 30 based
on their experiences and risk tolerances. If the costs associated
with operational risk can be determined, then economic and
risk-based life will converge.
Equipment Performance Data Base
Translating acceptable risk levels into performance and maintenance requirements is of great importance to power delivery organizations for both planning and operations. The previous section illustrated how risk assessment and mitigation require knowledge about asset performance and the ability to project future performance to apply the concepts of risk management to substation equipment. Other EPRI efforts are underway to gather the data needed for the failure data for risk assessments discussed and other asset management applications. This work,
Industry-wide Equipment Performance Database (IDB) can help provide important information to support the risk assessment process. (See epri.com for details.)
EPRI’s Transformer IDB [6] is the most mature database and is a collaborative effort to pool appropriate transformer operating and failure data in order to assemble a statistically valid population of many types of transformers. With sufficient data, it is possible to develop hazard rate curves for various asset management applications,
including those described above.
In addition to assessing risk at the individual unit level, utility managers often wish to assess risk for an entire fleet of equipment. One of the most pressing needs for new and better tools to support power delivery risk assessment is in the area of aging asset management and here fleet risk and performance assessment can play an important role.
It is well accepted that the risk of equipment condition deterioration and wear-out failures increases as equipment approaches the end of its useful service life however defined, i.e. ages. The rationale and timing of increased maintenance or capital investment decisions in anticipation of this increased risk have been traditionally left to historic patterns and engineering judgment. This may result in higher costs to the utility and its customers if investments are not optimally timed, i.e. if made too early, they may result in higher carrying costs; if made too late they may result in reduced service and higher failure costs.
An aging asset infrastructure makes it increasingly critical to identify equipment hazard functions of major asset classes and to understand the risks and influence of critical variables on equipment failure rates.
With appropriate failure rate information, risk-based approach models of the underlying distribution of failures or “bathtub” curve of each asset type can be developed to allow optimizing the risk-cost function in the maintenance and capital planning and decision-making processes.
Figure 6-3: Hypothetical equipment age distribution and failure rate curve
Historically, projected equipment failure rates were computed by dividing the number of past failures by the equipment-years considered in the studies. These historical failure rates were assumed to be constant throughout the equipment’s life and used for replacement and spares planning. However, a more general situation is depicted in Figure
6-3. A histogram showing the number of units of a particular equipment type in several age brackets has been superimposed on a failure rate curve that shows an increasing failure rate with age for that equipment type.
The age profile of the fleet reflects previous investment patterns. As time progresses, an increasingly larger percentage of the equipment population will move into the range of higher failure rates. This type of histogram gives rise to the term “asset walls.” A significant concentration of a particular asset in a group of adjoining age brackets looks similar to a wall moving forward in time.
As illustrated in Figure 3, the assumption of constant failure rate only applies if the equipment in question is operating in a flat portion of the hazard rate curve. Furthermore, many factors can influence equipment performance causing variations in equipment failure rates with time and usage. These could include equipment loading, manufacturer and maintenance history. A better understanding of how these additional stress factors affect equipment performance is required to correlate the relationship of various parameters with the probability distribution of equipment time to failure and failure rates among the fleets of different utilities.
Approaching asset walls, extended replacement lead times and new operating requirements make projections based on history increasingly risky. Planning maintenance, estimating future capital requirements and making the best effective capital funding decisions in the present requires a better understanding of asset performance projections over the long term. Understanding individual asset performance is important for tactical planning but understanding the collective performance for groups of asset types, fleets, has important implications for a comprehensive strategic asset and risk management program.
Approaching asset walls are of general industry concern. Running critical assets to failure may entail unacceptable financial and operating risks. Because of the skewed demographic distributions common in many utilities and the fact that significant numbers of units may be at or approaching the back end of the failure rate “bathtub curve,”
existing methods need improvement to provide informed long-range planning for effective management of this aging equipment.
Integral to the development of a fleet risk assessment is the ability to project the expected failures of the population at risk. In the basic case, this is calculated by convolving the hazard rate function with demographic data. The convolution is the sum of the products of the number of units in each age bin times the value of the hazard rate function for that specific age bin. The hazard rate function is fixed for a given population at the time of calculation; however, for each year or interval into the future, the demographic distribution moves to the right, causing more overlap and higher numbers of projected failures.
The key to successful application of this approach is to establish the proper hazard rate function. As previously discussed, it is not a straightforward task to generate an accurate curve for the assets of interest. Quantitative data on the hazard rate function for most transmission equipment types are sparse but the assessment of risk at the fleet level may not need the same level of confidence required for individual unit decisions.
To consider business risk, probabilistically derived failure projections can be developed as a function of the asset management strategy being considered. Consequential costs are then factored in to obtain the business risk for each strategy. Associated with each strategy is an investment cost. In theory, if investment costs are increased the business risk should decline. At some point, carrying out analyses for several asset management strategies could identify an acceptable trade-off between business risk and investment.
The general concept is to project the number of likely failures going forward, as a function of selected asset management strategies. The underlying density distributions for the selected hazard rates can be used in a Monte Carlo simulation process to project the ages of the failed units. The demographic distribution is then adjusted appropriately to move ahead in time. Using this methodology,
calculations can be performed for each proposed strategy on a year-by-year basis whereby the number of units that will fail is determined for the first year, the demographic distribution is adjusted to compensate for the failed units, and then the new demographic distribution convolved with the hazard rate function to calculate the number of failures in year two. The process of projecting the number of future failures can be continued for the number of years required for the associated risk analysis.
Integrating Risk Assessment into Asset Management
Figure 4 diagrams a framework for a PDAM approach to including risk considerations. Modeling performance usually requires data on past performance of similar facilities or equipment and some understanding of the mechanisms of aging and wear that contribute to a decline in performance over time. Knowledge of how operating stresses may influence asset degradation over time (i.e., aging models) is useful in this process. Expected future operating conditions are also required, for example increased transformer loading Specific, directed analysis may also be required to identify the underlying causes of performance gaps or an unexpected asset condition through root cause analysis and other forensic investigations. Using various analytical, statistical, and simulation tools, this process also determines such information as statistical failure data derived hazard curves, predicted risk-based end of life, condition-based triggers to support proactive asset maintenance or replacement, the implications of deferred maintenance, probability and consequences of failures and other risks, and future rating limitations.
There are two decisions in this process that the asset manager would like to make based on the present condition of the assets and levels of performance. The first is to determine whether the current state warrants taking some action now because of a current shortfall in meeting a desired risk or performance level. The second is to determine,
given the present state, whether there is sufficient concern about a future shortfall in risk or performance to initiate mitigating action now.
Information comes from equipment monitoring and maintenance and power system operating data, including any smart grid sensors and systems, to the Apply Assessment Algorithms process. These algorithms do not need to be complex to support more informed decisions. In the simplest case,
this could consist of merely determining whether a threshold level has been crossed. For example, a reliability index for a particular feeder circuit has exceeded a target level and design options for reconfiguration are initiated. Another example would be if the incurred maintenance costs for an individual piece of equipment exceed some percentage of its replacement cost, then options for replacement are explored. Other short-term assessments may be more complex and could include some risk assessment such as loading above some limit.
Figure 6-4: Integrating risk assessment into asset management
Long-term assessment algorithms may also be as simple as detecting a threshold level crossing. However, here the parameter crossing the threshold is not the current state but rather the projected future state. Obviously, this requires an ability to predict future performance. Obtaining this ability starts with Develop Stress/Aging Models. Building these models requires an identification of the mechanisms of deterioration for the asset or system and an evaluation of the rate of deterioration as a function of the various stresses (time,
loading, etc.). Again, models may not need to be complex to provide information for making better informed decisions.
Ideally the stress/aging models would represent the dynamic deterioration process with a set of equations that could be used in Perform Trending Performance/Failure Prediction to provide a forecast of future deterioration and the asset or system state for a future time. To be most useful for risk management decision support, the deterioration processes should be described probabilistically. With such models,
future states or performance levels could be represented as a probability distribution of states going forward. In some cases, it may be possible to develop trending algorithms that can use data from condition monitoring to automatically update these deterioration models,
make an assessment and provide an automatic notification of the need to consider taking action.
To predict future performance when the deterioration is not just a function of time but also of stress levels, the future stresses must also be predicted. If loading were one of the stress factors then a load forecast could be used for this purpose, for example. Future performance may also be affected by changes in operating or maintenance practices or by replacement of individual components within a system. Consequently,
these factors may also have to be accounted for in projecting future performance. Not every asset decision warrants a detailed analytical approach. In some cases, a simple extrapolation of past trends may be sufficient.
For some asset classes, the evaluation process could be automated.
Others may be too complex or too infrequent to justify anything more than a manual analysis, for example, deciding whether a feeder circuit needs to be upgraded to accommodate a new industrial customer connection. For some decisions, sufficient data may not be available to approach the problem mathematically and expert judgment may be the only solution. The generic models presented here describe the methodology for all of these situations and for good asset management the model should be applied across the power delivery system.
If current requirements are being met, then current practices may continue. If there are no projected problems in meeting future requirements, current practices may continue also. If there is a projected shortfall in meeting requirements in the future, there are two choices. Accept the possible future risk and continue current practices,
but catalogue the issue for future review, or decide on proactive action and develop options. Obviously, if current requirements are not being met, action must be taken also. Implicit in the Develop Options process is a determination of the causes of underperformance. Proposed actions are not limited to asset investments alone but also may include changes in operations and maintenance practices, design standards, training,
contracting and any other controllable action that impacts asset and system performance. Assessment of the possible risks from the candidate options will help in the decision process.
The ultimate objective is risk management through the selection of the most effective mitigation plan. The general approach for evaluating mitigation options is based on the premise that different options present different possible risk profiles.
Proper application of risk assessment tools can provide:
Analysis of candidate asset management strategies using quantitative
risk-based approaches that can reduce costs and improve other
corporate performance measures
Improved replacement needs projections that can facilitate savings
through improved strategic procurement arrangements and spares
strategies
Increased confidence in capital requirement projections for senior
management and regulatory scrutiny
Reduced operating contingencies probabilities or consequences
Chapter References
Guidelines for Power Delivery Asset Management, EPRI, Palo Alto, CA:
2005, 1010728
Lowrance, W.W. Of Acceptable Risk: Science and the Determination of
Safety, 1976, W. Kaufmann, Los Altos, CA.
Kaplan, S. and Garrick, B.J. “Quantitative Definition of Risk” Risk
Analysis 1, 1981, pp.11-27.
Li, W. Risk Assessment of Power Systems: Models, Methods, and
Applications, Wiley-IEEE Press, 2005.
Actionable Insights and Performance Metrics Derived from Analysis of
Industry-wide Power Transformer Data. EPRI, Palo Alto, CA. 2024.
3002029471.
1.7 - Chapter 7 - Implementing Asset Management
Introduction
There is no one optimum asset management implementation path. There are a wide variety of power delivery organizations, differing not only in size and type of service territory, but also in fundamental characteristics such as business unit responsibilities and ownership structure. Companies’ priorities and needs will vary. Therefore, to provide the most value to the widest audience, rather than an artificial
“cookbook” formula, this chapter will present a broad implementation outline and the development of the key attributes necessary for good practice asset management – be it at the enterprise, business unit, or department level. The goal is to provide descriptive guidance and examples.
Review of the PDAM models presented in Chapters 3 and 4 may lead one to conclude that implementing asset management is a daunting and complicated task. However, further analysis of the model elements shows that many of the processes and information links depicted are already in place in most utilities. Therefore, the reader should recognize that, by adopting an asset management philosophy as described in the preceding chapters and developing and applying the proper perspective and objectives as outlined here, significant progress towards implementing asset management concepts and practices can be accomplished. Many power delivery organizations can achieve this goal using resources and function already in place.
Moreover, today few utilities are adding personnel and many are losing experienced staff. The realities of the utility business environment and the need to maintain the day-to-day work dictate that a phased PDAM implementation is not only the most prudent but also the most practical approach for many companies. Although clearly the maximum benefits come from implementing asset management throughout the entire organization,
very few utilities would be comfortable undertaking the major shift in day-to-day thinking and organizational changes required to adopt a complete approach in one step.
Consequently, the underlying assumptions for the following discussion are:
No new resources, other than perhaps information systems, will be
available for PDAM implementation.
Initial implementation may not start at or be designed for the
enterprise level but may be completed in phases for specific
functions.
The primary goal here is to show how to utilize decision tools and decision processes, information flows and, most importantly, an asset management philosophy to modify existing business functions. The integration of asset management concepts into existing processes and functions can provide significant benefits by directing resources to efficiently achieve performance driven results.
Organizational Foundation of PDAM Implementation
It has been mentioned previously and may be obvious to many, but it is worth repeating: No sustainable changes can be made without management support and guidance.
Successful PDAM implementation requires a commitment to the asset management philosophy by senior management with authority appropriate to the level of implementation desired within the organization.
Implementation requires adapting new business values and process at all levels. Those involved must “buy in” to the approach.
Successful PDAM implementation requires a motivated and flexible staff focused on common goals and performance improvements.
Asset management is fueled by timely and accurate information exchange on asset condition, performance levels and desired results.
Successful PDAM implementation requires open communications and free flowing information across traditional organizational boundaries.
These three organizational foundation blocks are needed to successfully implement PDAM, or any other wide-ranging business change at any level,
and are assumed to be in place for the subsequent discussions. Figure
7-1 shows the relationship among these requirements and asset management core competencies, which will be discussed in detail later.
Figure 7-1: PDAM depends on a solid organizational foundation
Best Practices
Some discussion of the term “best practices” in reference to PDAM implementation may be helpful. Asset Management has been established as a formal discipline in recent years and a number of standards,
referenced in Chapter 2, have been published. The concepts and principles are well documented but the specific practices for applying these principles are less standardized. Even in industries were an asset management approach has been accepted for a number of years, procedures and processes continue to evolve and to be refined.
Given the short history of the application of asset management to power delivery and the wide diversity of power delivery organizations, it should be no surprise that generally acknowledged, specific (not generic) PDAM “best practices” are not well established. After all, even for areas such as equipment maintenance where utilities have decades of experience, there are still controversies about what constitutes a “best practice.”
Notwithstanding the above, there is no disagreement about what are the fundamental principles and concepts upon which a best practice asset management implementation is based. These concepts are well established and common to all of the recognized standards. This chapter will explore the implementation of a well-founded program based on “best practice principles” for PDAM.
Preliminary Steps
Regardless of scope, any asset management implementation should adhere to the best practice asset management principles, presented in Chapter
2. Best-practice asset management is about aligning key processes across the entire asset lifecycle to higher-level strategies and values. The foundation competencies relate to the decision-making processes and the key is to optimize tradeoffs across a variety of financial and non-financial metrics, rather than simply attempting to manage lifecycle cost or risk at the individual asset level. An asset management decision-making framework is guided by performance goals derived from the enterprise goals and policies, covers an extended time horizon,
provides for economic and engineering evaluations, and considers a broad range of assets investments on a consistent basis. PDAM provides for the economic assessment of tradeoffs between alternative actions and investment strategies from the network- or system-level perspective,
when warranted. At the same time, it allows for the more complete comparative analysis of options for individual projects.
Implementation would be initiated by a decision to adopt asset management principles to an organization by someone in authority. This decision may be prompted by a general desire to improve performance or in response to some identified shortcomings and may occur at the asset owner or senior management level or further down in the organization’s structure. Recognizing that the PDAM business model presented here is independent of an organization’s management structure or functional boundaries, a phased implementation may be planned not only at the enterprise level, but also for an individual department or section. It is only necessary that the deciding authority have a span of control commensurate with the scope of the desired implementation. The deciding authority should set the general boundaries and goals of the implementation and will be responsible for the governance of the resulting implementation.
After the decision to proceed, the first order of business is to put in place the staff structure needed to develop and manage an implementation plan. As for other activities of this nature, it is most desirable to have one individual with clear responsibility and authority for the entire implementation process. Ideally, those responsible for developing the implementation plan would also be responsible for executing the plan.
For an enterprise-wide implementation, the individual selected to lead would communicate with and represent the senior management or asset owners. For a phased, lower-level implementation, this individual would communicate with and represent management appropriate to the level of implementation. In most organizations, this stand-in for the asset owner function would correspond at least to the level to which the highest-level management affected by PDAM changes reports. Such a relationship is required to ensure that the higher-level goals and policies of the senior management are reflected in the implementation.
For example, to implement PDAM for substation maintenance, the manager to whom the manager of substation maintenance reports would fulfill the senior management or asset owner role.
The asset owner or senior management should assign implementation responsibility to one individual who possesses a broad understanding of the organization’s current state and a clear vision of its PDAM goals.
Most often, the selected individual would assemble a team with the skills and experience appropriate to the general boundaries and goals set forth by senior management at the implementation initiation. This team or steering committee is responsible for developing the detailed scope and schedule for implementation. Essentially, they are charged with answering the what, how, and when questions necessary to develop an actionable implementation plan.
What will be the scope and focus of the PDAM implementation plan?
What assets will be considered?
What processes will be adopted or modified?
Which organizations will be involved?
How will PDAM be implemented?
What decision tools will be used?
What are the required information systems?
How will responsibilities be assigned?
How will the required procedures be identified and developed?
When are the supporting subtasks and final implementation to be
accomplished?
How will progress be tracked and measured?
The assembled team should be cross-disciplinary with expertise in the engineering, business, and information systems of the affected business units. Specific aspects of the design of the implementation plan may require detailed knowledge and expertise and it is often desirable to form several subcommittees of subject matter experts to develop these plan segments. The team leader and his/her steering committee would be responsible for coordinating the work of the subcommittees and integrating their segments into a final implementation plan. A typical,
high level, workflow for developing an implementation plan is shown in Fig 7-2.
General Considerations for Implementation Plan Scope and Development
The goals and objectives of the initiating management are paramount in setting the implementation scope but there are a number of other factors that should be considered in developing an implementation plan. It is also important to realize that achieving some early success is valuable for building support and momentum for the more difficult challenges.
Consequently, selecting appropriate boundaries and intermediate goals can be effective in successfully achieving the ultimate desired results.
Some general considerations for scope development include:
Because implementing PDAM will most likely require changes in established processes and procedures, the scope should only include business units for which there are clear authority to implement change.
PDAM requires timely and accurate asset data. The initial scope should only include assets for which suitable data is available or provide and account for a means to acquire the necessary data.
PDAM utilizes decision support tools such as life cycle costs calculation and project trade-off evaluation systems. The initial scope should only include processes for which appropriate tools are available or provide and account for the development and implementation of suitable tools.
Figure 7-2: PDAM Implementation Plan Development.
Staff time and resources will most likely be split between PDAM implementation and the responsibilities of on-going business activities.
The selected scope should be appropriate for the available resources.
Early victories encourage management support and motivate team members.
The scope, where possible, should address some more readily attainable goals early in the implementation cycle.
Understanding where an organization’s current strengths and weaknesses are can be extremely helpful for developing a focused and efficient asset management implementation. An initial audit of the existing processes in comparison to PDAM practices can help to identify areas of weakness and prioritize desired improvements. Starting from the factors motivating the decision to move to PDAM, a review of existing business and technical practices, performance levels and critical issues can provide insight for the scope and sequence of implementation. In separate research, EPRI is developing and testing its Transmission Asset Management Implementation Maturity (TAMIM) methodology. The methodology provides a set of structured questions and algorithms to assess how well established are an organization’s capabilities—asset management behaviors, practices, and processes—needed to reliably and sustainably produce required outcomes in key asset management process areas, as well as the ability of the organization to continually incorporate improvement in these behaviors, practices, and processes. The results can be used to develop a fact-based roadmap for implementing new asset management functions.
Staff education is another worthwhile activity to be considered while developing the implementation plan. It can be expected that not every member of the steering committee and implementation teams will have the same degree of understanding of PDAM. In fact, since the term “asset management” is so loosely used, even within an organization, it is paramount that some effort be made to develop and communicate a common understanding both of PDAM principles and the implementation objectives.
Education can be accomplished through a series of activities, including workshops that address ever-wider audiences as PDAM progresses from planning to implementation.
Every utility’s starting point, resources, and needs are unique.
Consequently, one can expect that each implementation plan will be distinctly fashioned to meet the specific circumstances of the organization. The final plan should be well documented with clearly define responsibilities and objectives. Initiating the implementation is often the most difficult task but also key because this task sets the direction for all following efforts. Table 1 shows an example of one initial implementation plan. Although the specific details may differ depending on the plan scope, the table shows activities for a successful launch. Later chapters provide example utility implementations.
Table 7-1:
An example plan for the initial implementation
Goal
Objective
Result
Establish PDAM Governance
Assign lead for PDAM implementation
Accountability and control to ensure that activities are coordinated and properly sequenced
Form Implementation Steering Committee
Provide expertise required to formulate strategy
Foundation for proceeding with inputs and buy-in from affected functions
Prepare Initial Strategy
Define which assets are included and metrics and processes to be used
Sets the implementation scope and indicates level of effort required
Review Data Availability
Aligns initial strategy data requirements
Outlines data requirements and what analytical tools to consider
Educate Personnel About PDAM
Hold workshops on Asset Management theory and provide implementation expectations
More informed and better equipped staff
Plan Elements
Every implementation of PDAM may be unique but there are key characteristics of asset management whose development and deployment must be addressed by any plan built on best practice asset management principles. Before discussing implementation further, it may be helpful to review the definition of PDAM to identify these common elements. As discussed in Chapter 2, modern asset management is simultaneously a business philosophy, a process, and a set of technical tools. For the purposes of power delivery asset management as described in this report,
power delivery asset management is defined as “a structured, integrated series of processes aligned with business goals and values and designed to minimize the life-cycle costs and maximize the life cycle benefits of power delivery asset ownership, while providing required performance levels and sustaining the system forward.” Reviewing the elements of this definition will provide guidance on the essential principles of PDAM that must be included in any implementation regardless of scope.
Each utility’s implementation plan may be unique and distinctly fashioned to meet its specific circumstances, but all should address these principle elements of best practice asset management.
Key Definition Element: PDAM is a “structured program” because asset management is accomplished with documented and consistent processes and procedures. All decisions can be related to and support the organization’s goals and policies.
Required Implementation Plan Element: Every plan should ensure the
development and documentation of clearly defined linkages among the
organization’s goals and policies and measurable performance
criteria.
Key Definition Element: PDAM “minimizes the life-cycle costs of
power delivery asset ownership” by accounting for all costs and
benefits. Examining costs over an asset’s lifetime assures that all
costs are taken into account.
Required Implementation Plan Element: Every plan should ensure the
development and implementation of processes and asset management
tools to calculate asset life cycle costs and benefits and to
evaluate alternative investment options and optimize a portfolio of
projects on the basis of the organization’s goals and policies and
performance requirements.
Key Definition Element: PDAM “provides required performance levels”
because minimizing costs is not the only consideration. Performance
levels must also be considered. Even though asset management can
reduce costs, it also can improve reliability and performance by
focusing on the linkage between assets and desired system
performance levels. With a focus on asset condition and performance,
resources can be better allocated to where they best support the
organization’s goals and provide the greatest benefits at the least
cost.
Required Implementation Plan Element: Every plan should address the
availability or development of data sources to provide accurate and
timely information on asset condition and system performance,
including risk, and the means to compare them against desired
levels.
Key Definition Element: PDAM “sustains the system forward” because a
well-founded asset management implementation considers both
near-term (construction and maintenance) and long-term
(refurbishment and replacement) options. The utility planning
horizon is very long – typically, five to ten years or more and with
asset service lives of 40 years or more. Planning across this time
frame yields the information required for utility management to
understand current and future system needs and to fund them
properly, considering all potential costs and benefits.
Required Implementation Plan Element: Every plan should recognize
the long-term implications of asset decisions, including replacement
options, and provide for repeatable, formalized procedures and
information feedback to assess and modify processes and decision
criteria to improve performance and adjust for changing
circumstances over time.
For organizations with limited resources, a credible “first step” plan to adopt PDAM could consist of incorporating these key elements into existing business processes.
A generic flow chart for PDAM implementation is shown in Figure 3. The following discussion will address each of the four key elements of a comprehensive PDAM realization.
Linking Goals, Policies and Performance Criteria
Power Delivery Asset Management begins with the fundamental premise that all asset management decisions made by utilities should contribute to stakeholder values, as set forth in the organization’s goals and policies. PDAM applies this premise in decision processes at every level of the organization. The resulting alignment of decisions with criteria and value measures derived from the asset owner’s or senior management’s direction ensures that every asset management decision consistently supports the organization’s strategic objectives and delivers value to stakeholders.
Consequently, PDAM implementation should begin with a comprehensive process for defining organizational values, for example, financial considerations and non-financial considerations, customer satisfaction,
environmental stewardship, and risk. PDAM then provides a way of linking asset management decisions to higher organizational objectives. The goal is to establish clear and consistent criteria and direction across time and functions. Planning, priority setting, project delivery, and asset and system monitoring all need to be aligned with policy objectives and their associated performance measures. The alignment of goals and decision criteria requires a three-pronged effort.
Develop a corporate value matrix
Define values/goals
Set priorities
Determine trade-off weightings
Develop performance metrics
Linked to values/goals
Quantifiable
Observable
Develop risk boundaries
Key asset owner function
Overlays performance metrics
Figure 7-3: PDAM implementation process
Value Matrix
PDAM organizations have both financial and non-financial goals, such as reliability, safety and environmental quality. Furthermore, especially at the higher levels, goals often have qualitative as well as quantitative aspects. The kinds of values important to the wide range of power delivery stakeholders (stockholders, customers, community, etc.)
vary considerably. It is likely that some stakeholder values may compete and require trade-offs among them, and the values of different stakeholders will be expressed in different units of measure.
This diversity of goals can create challenges when setting priorities among various projects when their objectives are not similar and when standards of measurement are not comparable. These issues can complicate resource allocation and prevent the organization from achieving optimum performance. Thus, there is a need for a transparent, repeatable, and systematic framework within which to specify and value an organization’s goals and objectives in order to address these challenges.
The framework starts with the establishment of a comprehensive process to define the asset stakeholders’ values and goals. The objective is to translate “high level” goals, such as “improve customer satisfaction,”
into criteria that can be usefully applied to make judgments and decisions about asset investments. The desire is to be able to link clear, specific, and preferably quantifiable criteria for all asset management decision up through a well-defined hierarchy to corporate goals.
One approach to establishing this framework is to develop a corporate value model. A value model provides a means to determine the value,
related to the corporate goals, of the investments and operational activities that business units and employees undertake within their respective functional responsibilities. In general, a value model consists of three major components: a definition of the attributes that drive corporate value, a way of measuring the value of each attribute,
and a way of comparing value among the attributes. The topic is discussed in detail in the EPRI report Value Modeling and Measuring Key Performance Indicators for Power Delivery1 and much of the following section is adapted from that report. The topic is also well covered in the business and financial literature, for example,
Value-Focused Thinking: A Path to Creative Decisionmaking2.
Many utilities already have some kind of value model or value matrix in place.
Corporate Value Model
The purpose of a corporate value model is to quantify value, so that the values of various activities, such as capital investments, maintenance programs, and the like, can be measured and compared. Some kinds of values are readily measurable; for instance, the value of a kWh of electric power delivered is simply its price. Other kinds of value are more elusive; for example, what is the value of “reliable electric service?” Nevertheless, it is a fundamental principle of economics that value can be assigned to any tangible good or service. Correspondingly,
it is a principle of PDAM that resource allocation decisions are made on a consistent measurement basis, a value model, throughout the organization.
In general, three difficulties arise in trying to assign value:
precision, preference, and consensus. Precision means that one must describe precisely what one is trying to value. For example, what precisely does “reliable electric service” mean? Analyzing this question leads naturally to a breakdown of this attribute into various sub-attributes, such as “duration of outages” and “frequency of outages”
and also to specifying the means of measuring them. Preference means that one must be able to distinguish different levels of value for a particular attribute. For instance, clearly fewer outages are preferred to more outages, but how much more valuable are, say, two outage per year versus three? Consensus relates to the fact that among any group of decision-makers, preferences are likely to vary in ways that usually cannot be resolved objectively; thus, in order to have a credible tool to guide decision-making, the relevant people must reach a consensus about the value model. These difficulties can be overcome using a systematic process for defining a value model with the following characteristics:
Level playing field
Resolve differences of opinion rationally
Defensible logic for peer review
Transparent analysis
Completeness with respect to performance measures
Bias- and error-free
Practically applicable with respect to cost and time
Compatible with existing business practices
The foundation of value modeling is a set of attributes. Since high-level, asset owner objectives, such as reliable electric service,
are usually not defined precisely enough to measure, the attributes usually must be refined by defining component sub-attributes. Thus, the attributes of value form a hierarchy, with the high-level corporate goals at the top. Successive levels in the hierarchy represent increasing specificity, until, at the bottom level, the attributes are readily observable and fundamentally measurable. The hierarchy defines each component of value to which an activity may contribute and establishes the relationship between that value attribute and the overarching corporate goals. Development of the hierarchy requires definition of each value attribute starting at the highest level and including, as necessary, additional levels of sub-attribute definition to adequately capture all unique sources of value. An example of a value hierarchy is shown in Figure 4. One possible approach is discussed below.
Figure 7-4: Example value model hierarchy for power delivery
Each attribute is measured in natural units. For many attributes of interest to power delivery, natural units are readily observable and measurable quantities, such as number of outages per year or average outage duration in minutes, and vary depending on the attribute. The natural unit of sustained outages is usually characterized as the number of outages per year. The natural unit of overload is a percentage. A natural unit for safety is number of lost-time accidents per year.
Once attributes and their corresponding sub-attributes have been identified and their measurement units defined, their scale values over a dimensionless range, for example 1 to 10 points, must be determined.
The natural units are converted to scale values that indicate the relative importance of different performance levels. The relative importance of each level of a particular attribute is measured by the scaled value*.*
For example, suppose the measurement unit for “reliable electric service” is System Average Interruption Duration Index or SAIDI. This unit is well defined by industry standards and can be readily calculated and tracked. However, the measurement unit tells us nothing about the value of changing the index. For instance, if a certain maintenance activity reduces customer outage minutes by 50% compared with not doing the maintenance and another reduces it by 25%, is the first twice as valuable as the second? Clearly, the relative values are a judgment decision that can vary from organization to organization. Consequently,
scaled values need not be linearly proportional to the natural units.
For example, the scale for outage duration might range from 0 (on a 1 to
10 scale) for a 24-hour or longer outage to 10 for a 0-hour outage (no outage), with an intermediate value of 2 for a 1-hour outage. This would imply that it is four times more important to avert a one-hour outage
(gaining 8 scale points) than it is to reduce a 24-hour outage to 1 hour
(gaining 2 scale points). These values are for illustration purposes only.
Developing scales for qualitative attributes presents further challenges, since a qualitative attribute has no “natural units.” In this case, a scale has to be defined by descriptive statements indicating the various levels of value, and there is no direct way to determine the value of changing levels. Expert judgments must be utilized to assign value to changes in a qualitative scale.
While scales measure the value of changing an individual attribute, they say nothing about the relative value among different attributes. In order to compare value between two activities that affect multiple attributes, it is necessary to have a way to compare values among those attributes. For instance, one might ask “How much is reducing residential customer outages by 25% worth compared with addressing an issue that results in a transmission line overload during normal operations lasting 100 hours per year?”
The weighting process starts at the lowest levels of the attribute tree.
At each step of the process, stakeholders are asked to compare the relative importance of changing two or more attributes. When all the sub-attributes that roll up to a particular attribute have been weighted, the process moves to the next set of attributes, and when all of the attributes at a particular level of the tree have been weighted the process moves up to the next level. The key to eliciting accurate weights is to structure a process that enables people to compare a small number of attributes at a time and to always look at specific examples of the value impacts in order to anchor the comparisons in concrete terms.
The value model described above translates all attribute measures,
including dollars, to a dimensionless scale in order to make comparisons across investments that produce different attributes or different mixes of the same attributes. The dimensionless aspect of this scale emphasizes that comparisons are made on a relative value basis. Other valuation model approaches translate all attributes to dollar values in order to make comparisons. For example, each residential customer interruption could be valued at $30 and an improvement of one point in a customer satisfaction survey valued at $500,000.4\ 5
Each approach has its advantages. Putting all attribute values on a dollar scale relates well to one of the core PDAM objectives of minimizing lifecycle costs and facilitates discussions with the financial part of the organization. Dimensionless values and the relative value model discussed above can better handle potentially issues that are difficult to monetize, such as value of reliability, or difficult to quantify, such as safety and community relations. This factor is particularly relevant in the power delivery business, where many assets do not have readily observable market values and where many performance criteria are set by a regulator rather than by a market.
Within a PDAM implementation, these two value models are not necessarily mutually exclusive. With care, the two approaches can be combined. For example, the dollar approach could be used within specific budget classifications and the dimensionless approach to construct portfolios of multiple budget classifications. This can work if the specific classification of investments to be valued in dollars has only a few,
easily quantifiable attributes. However, the one-dimensional dollar-value approach becomes more difficult to justify the further up the organization the application. This is because asset owner’s goal and objectives are, by their very nature, multidimensional (e.g. financial,
public safety, regulatory relations). Putting all attributes on a dollar
(financial) basis can distort the decision process at the asset-owner level.
Other methods develop relative valuations without using any numerical estimates at all.7 There are a number of possible approaches to assigning values to power delivery project and activity attributes.
However, to be valid for the purposes of PDAM, they must possess all of the characteristics listed for the above value model. Model selection is therefore part of the implementation effort, and much benefit can be derived from having even simple models.
Performance Metrics
One advantage of the value model approach discussed above is that the natural units defined in the model development can also be used directly as performance measures. Performance measures are observable,
quantifiable measures that align with project and process objectives.
They provide the means to track progress toward meeting the objectives.
Performance targets are specific values of performance measures that provide the level expected to be attained. This target may be set for a specific time period and with the understanding of a particular level of funding. It provides the bar against which actual performance data will be compared. For example, SAIFI is not only a natural unit for measuring value but also a way to measure performance.
All natural units that come out of the construction of a corporate value model can be used also as performance measures. However, not every performance measure will be defined in the development of a corporate value model. As an example, line-miles covered per $1,000 of vegetation management expenditure can be an informative measure to review and track, a performance metric, but would not come from a corporate value model, although of course its effect could impact a model attribute.
There are, in fact, two general classifications of performance metrics of interest to PDAM. Results metrics measure what has been accomplished.
These are always lagging indicators. Process metrics measure how the results are or were achieved and can be either leading or lagging indicators. As for the example above, results metrics are usually closely related to attribute value metrics. One conceptual approach to developing a list of performance metrics is to use a process model formulation. All natural units defined in the development of the corporate value model can be considered as the outputs of various processes and can be measured by their natural units. The inputs to the process that produce the result are measured by process metrics. Going back to the SAIFI example, the index is the result of a process with many inputs that can be measured such as vegetation management mentioned above and equipment maintenance, each of which can be considered as a sub-process with its own process metrics (e.g. maintenance backlog). One can see that the approach to developing a set of performance metrics is similar to the hierarchal corporate value model development with attributes and sub-attributes described above.
Defining and measuring performance metrics of both classifications is important for good asset management. It is important not only to direct resources properly to maximize value but also to utilize the resources efficiently and effectively in attaining that value. In addition, good asset managers want to respond to deviations in results metrics quickly.
To do this, it is important to know what influences the result metric.
Process metrics provide this information. Furthermore, most results metrics are lagging indicators. This is to be expected since they are the output of a process. A distribution manager only knows that the SAIFI metric has gone below target or has not been reached after the fact. Process metrics can be leading indicators (e.g. maintenance backlog). Tracking and managing them can improve the performance of the result measure.
For power delivery, there are usually many inputs to a process that has a single result, and one should be cautious about developing a performance measure for each. The potential benefit should out-weigh the cost of data collection and storage and it makes no sense to gather information that will not be acted upon.
Key Performance Indicators
Key performance indicators (KPIs) are, in a general sense, the same as the performance metrics discussed above. However, the term has come to be associated with metrics that are tracked and reported to a higher level of authority. For this reason, KPIs usually focus on measuring accomplishments or results. The commonly used reliability indices are an example. They directly measure a dimension of customer service level that is the result of many factors – design, maintenance, capital investments, etc. Trigger parameters in performance-based rates can be considered as KPIs and they too are the result of many separate activities. Because so many factors can influence a high level KPI, it can be difficult to gain insight when some corrective action is required. For this reason, many organizations develop lower level KPI,
for example maintenance metrics such as number of backlog maintenance orders. In fact, KPIs can be useful at any level of the organization if they are properly chosen. Good KPIs should:
Focus on accomplishments rather than activities
Utilize readily available metrics
Provide meaningful indication of performance to all levels of the organization
Promote improvement
Allow external comparison (benchmarking)
Communicate progress
Selection of KPIs is ultimately a company-specific decision but there is similarity among power delivery organization for the higher-level KPIs.
An example of one utility’s use of KPIs to develop an overall measure of the organization’s performance is given in Table 2 below:
Table 2 :
Example key performance indicators
Category
KPIs
Weight (%)
Financial
Free Cash Flow
20
ROA
10
Total Costs/MWh
5
Operating Revenue/Total Asset
5
40
Customer Service
Grid Frequency Fluctuation
4
Grid Voltage Fluctuation (transmission voltage levels)
5
Number of Customer interruptions/number
of distribution substations
8
Sum of the durations of all customer
interruptions/number of distribution substations
8
25
Social responsibility/
Recycle rate of industrial wastes
3
Environmental Protection
Recovery rate of SF6 gas
2
5
Work Processes
Capital expenditure
4
Capital expenditure/kW
4
Maintenance expense
4
Maintenance expense/kW
4
Substation output (kW)/Number of employees
2.5
Transmission line length/Number of employees
2.5
Transmission loss rate
2
Thermal generation fuel consumption rate
2
25
Employees
Number of employee injuries
2
Number of official qualifications acquired by employees
2
Employee satisfaction
1
5
Total
100
There are dozens of KPI definitions that have been developed by various power delivery organizations to track most activities.
A new category of KPI that has special meaning for asset management would be one that could be used to measure how well asset management practices are being applied in the organization. Some examples might include:
Percentage of capital dollars spent that have been allocated through
a trade-off process
Percentage of maintenance tasks that are formally linked to a
performance level criteria
Accuracy of predicted performance level versus actual level
Actual equipment condition versus previously predicted level
The development of asset management process KPIs has not yet
received much attention.
Risk Boundaries
It is not enough for the asset owner only to set performance goals. The owner must also set some boundaries on how much the organization is ready to risk achieving those goals. Risk can be considered as a measure of the uncertainty of business performance and as such is closely tied to corporate goals. There are several definitions of risk centered on whether unexpected positive or negative or both outcomes are included.
For power delivery, defining risk at the asset owner level as a loss or negative result is more appropriate. Because power delivery is a regulated activity, there are few opportunities to achieve unexpected,
materially positive outcomes. Utility managers in power delivery are risk adverse in the sense that they concentrate on minimizing negative results. Formally risk is defined as the product of the probability of a hazard causing loss occurring and the consequences of that loss.
The subject of risk has been extensively studied for financial assets and is generally considered to be the risk of not achieving the expected financial return. Risk has also been studied in detail for the power generation and energy trading side of the utility business. Here the desire is to assess energy portfolio exposures to commodity markets and customer loads, evaluate overall portfolio risk in terms of cash-flow-at-risk or value-at-risk, and assist in designing portfolio risk management programs.
For power delivery, risk management is not so well formalized at the asset owner level. However, there has been much discussion and work done at the level of individual power delivery component assets. It is important to clarify the distinction between the two and an example will help to illustrate the differences. The loss of an important asset, say a tie line power transformer, will impact one or more performance measures (e.g. equipment availability, wheeling revenue) and this risk can be evaluated by valuing the potential for a short-fall in the affected performance measures (thereby relating this risk directly back through the value model to the asset owner’s values) and the probability of failure. This is asset level risk and is most often the responsibility of the asset manager. What the value model does not define is how much asset-level risk the asset owner may be willing to accept. For the tie-line transformer example, for how much revenue and for how long is it acceptable to the asset owner to overload the unit,
while at the same time increasing the probability of failure, in order to increase income? Deciding the acceptable level of this risk is the asset owner’s prerogative. Another illustration of risk management that requires an asset owner perspective would be the evaluation of the risk exposure of not doing something. One example would be investing now to replace functioning units versus the unanticipated need for a large capital investment over a short time to replace a significant population of aging equipment, which may require unplanned financing.
There is another kind of asset level risk and that is expressed as the probability that a particular asset investment will achieve the expected benefits over the time frame of interest. For example: Will the new monitoring system provide the information necessary to implement condition-based maintenance and reduce preventive maintenance? This type of risk is similar in form to that used to quantify financial portfolio risk, and its management is the responsibility of the asset manager.
Setting risk boundaries at the asset owner level is now a normal part of the deregulated power generation business but is not often explicitly treated in the power delivery sector. One exception is in the area of performance-based rates. In such cases, the asset owner has directly expressed a risk valuation through the acceptance of performance standards and associated monetary adjustments for deviations. In order for the asset manager to manage asset level risk in accordance with the asset owner’s wishes, he/she needs to understand the owner’s risk boundaries. This information is necessary to properly manage asset-level risks whenever the outcome has the potential to impact an area not in the asset manager’s area of responsibility (e.g. corporate finance,
regulatory relations). At the very least, for communicating a relative sense of risk tolerance, qualitative statements can suffice if they are informative and meaningful, that is give a sense of relative priority,
if quantitative statements are not possible.
Implementing the Link between Goals, Policies, and Performance Criteria
Developing the corporate value model and performance and risk metrics is a cornerstone activity of PDAM implementation. The results of this effort influence all other implementation steps and the subsequent operation of the asset management processes. It is also the step that requires the most interaction with the asset owner and senior management. To achieve success, sufficient attention, resources, time,
and expertise must be made available to accomplish these tasks.
Often, this step starts with a workshop that includes experts from all of the functional areas included in the implementation as well as the internal customers of those functions, if they are not part of the initial implementation, and representatives of the asset owner. A broad representation is required to capture all values and to promote buy-in and ownership of the results. Due to the importance and visibility of this step, it may be advisable that all members of the implementation committee participate in this workshop. An outside facilitator with experience in building value models is often helpful.
An iterative approach provides for step-wise approvals to ensure alignment with asset-owner values and to make efficient use of senior management resources. The process steps include the following, with the responsible party identified:
Preliminary top-down identification of value categories – by
committee and asset owner
Identification of attributes and their measures – by subcommittees
of experts
Review of attribute list – by committee
Approval of attribute list – by asset owner
Identification of sub-attributes, their measures and weights – by
subcommittees of experts
Roll up to attribute level and review – by committee
Approval of roll-up – asset owner
Develop attribute weighting – by asset owner with committee input
Documentation – by committee
The importance of good documentation cannot be over emphasized. The
recorded results of this process should be precise and unambiguous
and available to all PDAM participants.
Asset Information Considerations
The second major task in the PDAM implementation as shown in Figure 3
is to address the requirements for data and asset information. The details of this effort depend on the implementation scope, the management processes already in place, the level of data integration in place, and the organization’s information technology philosophy and procedures. Consequently, this section will provide a high-level overview of this portion of implementation. Asset information considerations are closely related to and should be coordinated with the work on developing plans for asset management tools discussed in the next section. It may be helpful to have one subcommittee to work on both aspects or, at the least, liaison representatives attending both committee meetings.
The tasks described in the preceding section to develop attributes,
their metrics, and related result and process measurements will generate a preliminary list of the data required for the intended PDAM implementation. The next step is to compare the data requirements against existing data sources within the organization. Much of the data will reside in the existing enterprise management system, asset registers, and work management system. It is not necessary, and in fact unlikely, that most of the data identified will reside in one data repository. However, it is important that the required data be accessible and in a useable format. Data integration and sharing are critical issues for PDAM implementation. Integration issues will be further addressed in the following section on tools.
All of the general considerations for incorporating and governing data into any core business functions, including accuracy, timeliness, and security, apply when identifying and qualifying data sources for PDAM.
The information requirements for PDAM also include the need to be able to present analysis results and information to decision makers in an understandable and efficient manner.
Work on this portion of the implementation plan should include the development of a data strategy addressing all of the important considerations. Often, a separate subcommittee is formed to handle this task. The data strategy should be developed with inputs from those business units directly affected by the implementation scope and with strong support from the organization’s information technology group. An outline of the process for developing a data strategy is shown in Figure
7-5.
Most power delivery organizations already have many of the ingredients of an asset management information system including various tools,
models and databases. Some of the more common components that are relevant to asset management include the following:
Maintenance Management Systems. These systems typically include asset inventory, work order management and history, and condition assessment.
Customer Information System. These systems commonly contain payment history, work order history by customer location, and customer correspondence including compliance and billing data.
Finance Databases and Models. These databases may include billing,
accounts receivable, accounts payable, tax data, budgeting and forecasting, valuations, and debt management
Financial Models for Developing Customer Rates and Replacement Planning Models may also exist in some utilities.
Geographic Information Systems (GIS). Since much of a utility’s asset information can be tied to a geographic location, another significant element of an asset management system is a CADD or GIS-based map of fixed assets and related data.
Capital Planning Data. Most utilities have some type of database related to their facility capital improvement projects.
Business Processes. Individual functional groups within a utility often maintain some information related to their key business processes and standard operating procedures. Sometimes performance measures or targets may also be included.
Figure 7-5: Developing a Data Strategy for PDAM Implementation
An audit of the existing information systems should be part of the strategy development. The audit should include:
Currently available data
Accessibility
Location
Source
Where and how obtained
Update frequency
Data Base Structure
Format
Size
Quality
Timeliness
Accuracy
Integrity
Consistency
Completeness
Redundancy
Cost to access
Projected future availability
Current uses of existing data
Business processes
Reports
Communications requirements
Applicable IT and data standards
The specified data requirements should include sufficient information on assets, performance and cost to support the full range of business processes included in the implementation scope. The general classes of data that will be needed for almost any scale of PDAM implementation are:
Asset Inventories
Current Asset Condition
System Performance
Costs and Financial Data
Caution should be exercised in specifying new data sources. Costs for collecting, storing and maintaining data must be considered and only data for which there is a clearly defined use and benefit should be included in the strategy. No data should be collected more than once. If individual processes require the same kind of information, but in different formats, or at different levels of detail, then automated methods should be established for deriving the necessary information from the primary source.
It is quite likely that much of the data required will already exist somewhere in the organization. (An important exception is the lack of good data for projecting future performance of power delivery system components as a function of age and stress. This topic will be discussed in the following section on asset management tools.) In some cases, if the data is not directly available, then it may be possible to take advantage of an existing data collection process to acquire it. It may also be possible to stage the migration of data to provide near-term improvement while planning for longer-term redevelopment.
PDAM Support Tools
In theory, asset management principles could be implemented by utilizing manual data entry and spreadsheet calculations, but the process would be inefficient and would not take advantage of data residing in existing power delivery information systems. The number of assets involved and the complex, multi-disciplined, and data-intensive nature of PDAM implementation necessitates automation of the decision support tools and associated data systems. Only with the right inputs, can PDAM give decision-makers improved quantitative information regarding resource allocation. That is why the development of a solid data strategy as described above is so critical. However, to support strategic long-term planning and life cycle management, portfolio evaluation, and risk assessment and management at multiple levels within the organization,
the data must be used effectively.
Currently, power delivery asset organizations can meet the goals discussed above to some extent by using a combination of separate off-the-shelf or utility-specific software applications or spreadsheets.
However, the current lack of unified software tools can cause the analyses to be labor intensive and time consuming and can produce only isolated pockets of information. Further, certain gaps exist in the available tools (e.g., the lack of screening analysis automation tools),
even when they are combined. Complicating matters, much of the needed data and nearly all of the actions evaluated in PDAM are stored in or controlled by existing enterprise systems, asset registers, and work management systems.
The detailed process descriptions presented in Chapter 3 can be used to provide a broad functional specification for the tools that could be applied to implement or support that process. In general, there are three categories of tools required for PDAM.
Information Tools – Tools specifically designed to provide policy-level information to support review and decision-making by executives and managers desiring an overview. These tools present summarized and categorized information, for example by geographic area,
Tradeoff Analysis Tools – Tools designed to assist with tradeoff analysis across asset classes, program categories, and types of investment, making use of comparative analyses of costs, benefits, and performance measures. Included here are Cost/Benefit Analysis tools that provide analysis to be used as a basis to evaluate different categories of candidate projects. When structured in a life cycle cost context,
these tools provide an economic framework for analyzing capital-maintenance and capital-operational tradeoffs.
Assessment Tools – Tools designed to provide a measure of the current or future state of an asset’s condition or the current or future level of performance of an asset or system.
Available Tools
The scope of the planned PDAM implementation, the work done to develop performance metrics and the business model shown in Figure 3 will determine the specific decisions processes required for any given implementation. In turn, this will indicate in which business processes and where in the work flow decision support tools would be desirable.
EPRI has developed decision support tools and analyses methodologies along with data models for some transmission assets that can support PDAM. The reader is referred to epri.com and the Transmission Asset Management Analytics program for an up-to-date listing of the available information and on-going developments.
EPRI continues with work to develop a comprehensive set of models,
methodologies and tools to support asset management implementation for power delivery at the enterprise level and new tools will be released in the coming years
Implementation Considerations
Choosing decision support tools to be included in the PDAM implementation will require personnel with knowledge of the asset management functionality desired and also those with information technology expertise. Each organization’s plan will be unique and will depend not only on the scope but also on the number and characteristics of existing data repositories and information systems. Reviewing the available data and the functions required by the implementation scope will indicate where tools will be most useful. All of the care and safeguards of any IT implementation should be incorporated into the plan.
Developing Implementation Plans for Process Flows
The fourth major task of implementation plan development (Figure 3) is to layout the workflows, develop procedures, and assign responsibilities for the new PDAM business processes. These must then be incorporated into the existing organizational structure or the structure modified to accommodate any new processes. Management systems and reporting mechanisms must then be put in place to monitor and control PDAM execution. This is a crucial task because it directly affects how well PDAM will be executed. Since the results of this effort affect how people view and carry out their day-to-day jobs, particular care should be taken in forming the planning team responsible for this phase of the implementation.
The details of this piece of the plan will be specific and customized to the circumstances of the organization and scope of the implementation but, as with other aspects of the implementation, there are certain characteristics that should be considered in order to adhere to the best practice principles of asset management.
The overarching principle of asset management is the linking of asset owner’s goals and policy to asset and system performance and the investments that affect that performance. To accomplish this objective,
formal mechanisms and procedures must be put in place and responsibilities assigned to link policy and goals to investment priorities and performance levels. The procedures that are required include:
Value model development
Performance measures development and dissemination
Consistent project and program evaluation and tradeoff methodologies
The models presented in the preceding chapters provide a good starting point for establishing data paths and work sequences regardless of the implementation scope. For example, procedures should be established to ensure that all proposed actions are evaluated with regard to their contribution to achieving performance measures tied to the organization’s goals (P8 in Figure 1).
The final PDAM implementation should be designed to make certain that there are review mechanisms and feedback loops to monitor the quality of the individual processes once they are underway. Responsibility definitions, objectives, tracking methods, scheduled reviews and process to update milestones, should be documented. Members of the implementation committee may assume some permanent oversight role to insure that PDAM is a “living program” that can facilitate continuous improvement.
Final Thoughts on Implementation
Regardless of how comprehensive or how limited the chosen PDAM implementation scope is, in the end implementation represents a number of specific tasks performed by each business unit charged with improving its particular practices, use of information, and decision processes and criteria. In moving forward with the specific details of any implementation, it is important that those responsible also maintain an overall view of the process. Careful coordination will be needed to ensure that the individual efforts of each affected group support the overall objectives of the organization, and that they are aligned with one another and with the various support activities required. This coordination is the responsibility of the senior management representatives on the implementation planning committee.
Implementation of PDAM requires change. The magnitude of the change depends on the current practices of the organization but may very well necessitate a culture shift in philosophy. Business processes,
procedures, objectives, success measures, and relationships are all subject to review during PDAM implementation. Therefore, throughout the process, the implementation committee and the asset owner must educate and communicate with those affected and present arguments for PDAM in order to generate maximum acceptance and support.
Chapter References
Value Modeling and Measuring Key Performance Indicators for Power
Delivery. EPRI, Palo Alto, CA: 2007. 1012502
“Value-Focused Thinking: A Path to Creative Decision-making”
(Paperback), R. Keeney, Harvard University Press 1996
“Project Prioritization,” Presentation EEI TD& M Conference Spring
2003 St. Louis, Mo. M. Thaden D. O’Neill
“Lessons Learned - Resource Allocation based on Multi- Objective
Decision Analysis”, E. Martin and M. W. Merkhofer, Proceedings: EPRI
Power Delivery Asset Management Workshop: New York City 2003 EPRI
Report 1008965 Jul 2003.
“A Novel Methodology for Management Decision in an Electrical
Distribution Utility” R. A. Slavickas and M. A. El-Kady IEEE
TRANSACTIONS ON POWER DELIVERY, VOL. 19, NO. 1, JANUARY 2004
1.8 - Chapter 8 - Utility Example -- Transmission Substation Asset Management - A BOTTOM-UP Approach
Introduction
This chapter presents an example of how an electric utility in the Northeast United States developed an asset management program for transmission substations. The chapter focuses on the implementation and development of the organizational structure, people, and tools used in the program to achieve value from the organization’s assets.
Overview / History
The development of this program could be described as “bottom-up.” This is not to infer that the effort was without management support, but instead indicates that the program did not result from a top-level decision to develop asset management capabilities in a specific time using a formal roadmap. It did not start from a planned implementation of PDAM as described in preceding chapters. Instead, the development of this program was “organic,” in that it started with the formation of equipment peer teams and grew as the subject matter experts identified problems and developed solutions, and as the capabilities of the company matured. Nonetheless, the results adhere to and have many elements in common with PDAM concepts.
Unlike the example of another company that took a top-down approach (see Chapter 8), because of the way this program developed, it is not easy to specify a timeline of when various elements of the program developed.
The utility reports that much of the work was done in parallel over a span of about ten years but this timeframe represents only one utility’s experience given their particular circumstances and resources.
The individuals interviewed for this example emphasized the following:
Much progress has been made through grass-roots effort. Progress is
made when an individual or group champions something and shows why
change is needed.
Even then, some groups or areas may not readily embrace process
changes and recommendations for improvement. Sometimes it takes a
bad event caused by an issue already addressed by other parts of the
organization to convince employees of the need to change. This is a
disadvantage of the bottom-up approach, as there is no directive
from company senior management to drive change and consistency of
work processes.
When processes and systems help employees do their jobs and protect
them from mistakes, most people get on board and work to make it
better. Today, this company sees much more involvement and feedback
from field maintenance personnel than ever before. The asset
management organization must act on such feedback to have
credibility.
Organizational Structure
The organizational chart (Figure 1) shows where the asset management program fits into this organization.
Figure 8-1:: Simplified organizational chart showing the position of asset management
Creation of the Maintenance Program
Initial Formation of Equipment Peer Teams
The asset management program started with the formation of a single
“equipment peer team” for breakers. The peer team consisted of maintenance supervisors, field engineers, and equipment engineering subject matter experts from each of the geographical regions of the company. Due to the relatively compact service territory of this utility, the team initially met in person each month.
The team was first asked to identify the things they were concerned about with respect to breakers. These discussions eventually revealed a general desire to investigate how the company was performing breaker maintenance. At that time, the utility was performing breaker preventative maintenance strictly on time-based intervals regardless of breaker condition. Members of the team suggested that it would be more cost effective to replace bad-actor breakers than to continue performing time-based maintenance. Eventually, the decision was made to eliminate much of the time-based breaker maintenance, perform some diagnostic testing on a time schedule, avoid rebuilding breakers with no apparent issues, and focus on replacing problem equipment. This convinced the members of the team that the peer team could make a difference, and since the team was driving replacement decisions, subject matter experts from each region had a vested interest in actively participating on the team.
Maturation of the Peer Team Concept
Following the success of the initial breaker peer team, teams were formed for transformers, circuit switchers, disconnect switches,
batteries and DC systems, and capacitor banks. Participants on the peer teams included asset managers, field personnel, equipment specialist engineers, training group, capital planning group, maintenance procedure writers, and computerized maintenance management system (CMMS)
administrators. Initially, teams met quarterly, but when most equipment issues were addressed and a formal corrective action program was instituted, formal peer team meetings became infrequent.
The scope of the teams eventually expanded to include the following topics:
Maintenance strategies
Capital budgets
Maintenance Procedures
Operating events (e.g., one event led to the introduction of breaker
remote racking devices)
Training and training improvements
Emerging issues
In between meetings, the peer team uses their distribution list as a forum to help each other resolve issues.
Next Steps in Peer Team Structure
Recently, the structure of the equipment peer teams has changed to improve decision-making. There is now a core group for each peer team that makes decisions based on equipment performance, cost, and input from the larger group.
(Note that the following benefits statements—and other benefits appearing in shaded boxes—are written as if the company had identified objectives before launching that part of the program. However, since the program was developed incrementally, some of these benefits are recognizable only in hindsight.)
Benefits of the equipment peer teams
Identify improvements in maintenance strategy, including
preventative maintenance scope and frequency (Reference [1],
maintenance strategies, preventative maintenance optimization,
condition-based maintenance, continuing equipment reliability
improvement)
Identify equipment bad actors that should be programmatically
replaced (Reference [1], equipment bad actor identification,
condition/performance monitoring, long-term planning)
Prioritize capital replacement projects (Reference [1], capital
replacement program; long term planning)
Identify improvements to work instructions and training (Reference
[1], training, precision maintenance procedures, PM
implementation)
Create standard labor estimates for all preventive maintenance
(Reference [1], work measurement, PM implementation)
Provide a forum for all stakeholders to influence the health of the
system (Reference [1], employee engagement)
Provide a forum to share practices and operating experience so that
mistakes were not repeated and all regions of the company improved,
corrective action)
Focus on Data Collection
As the breaker peer team began thinking about significantly reducing time-based maintenance, they recognized that there was not an easy way to capture and trend the maintenance information needed to make condition-based decisions. Breaker PM data sheets, for example, were kept on paper and there was no central storage repository. This was a concern, as although the CMMS contained equipment, PM, and work order information, the level of detail needed for asset managers and equipment engineers to assess the condition of the equipment and initiate action based on condition was not electronically available.
The first step in addressing this problem was to scan existing data sheets into a central repository. The next step was to implement a commercially available plug-in for the CMMS that facilitated the process of collecting structured and detailed data. The utility has spent 10
years developing their use of this application. Although the plug-in is designed for use on mobile platforms (allowing data to be entered at the job site), it was not initially used that way. As better mobile hardware became available, cybersecurity issues resolved, and the understanding of the tool matured, the utility has implemented the use of mobile tools at the job sites.
Details of Data Collection Tool Implementation
Since the data collection tool can now be used in the field, work instructions for inspections and maintenance are provided in the tool via a series of questions that field personnel must answer. Based on these answers, the tool may present additional questions to collect more information. The system provides hyperlinks to detailed job procedures,
so the current procedure is always available to the field worker.
Test and inspection criteria are built into the tool, so there is no question about whether a result is satisfactory, and post-test review for out-of-spec results is not necessary. Any result that is not within specification immediately generates an “alarm” for the person executing the test or inspection, and an email is sent to the reliability engineer. This provides visibility to what is happening on the system,
and corrections can sometimes be made immediately.
The tool also allows field personnel to give feedback on the procedure itself, record causes for delays in completing the work, or to comment on other aspects of the work, such as maintenance frequency.
Through collection of data electronically, at the source, using a question-and-answer format that allows data to be collected in fields that can be queried (rather than in free text “finishing comments”),
this tool provides information to the reliability engineer that would not be possible to see with paper records. The utility reports that inspection and maintenance results have significantly improved.
Benefits of the mobile data collection tool
Improve data collection, thereby enabling trending and analysis
(Reference [1], business intelligence/analytics)
Ensure maintenance and inspections are completed as intended and
compliance with all requirements (Reference [1], precision
maintenance procedures)
Provide immediate feedback to management on work performed
(Reference [1], work measurement, employee performance)
Simplifies tasks for maintenance and inspection performers; reduces
review effort for field supervisors; creating obvious value; leads
to buy-in (Reference [1], employee engagement, supervision)
Work Planning and Scheduling
Before about 2014, only work that required an outage was planned, and that planning was done on whiteboards. Maintenance activities that did not require an outage were not formally planned.
Around 2014, one of the field groups asked for tools to plan their work.
Out of this request, a software planning tool was built in-house. This software builds an electronic work package (in essence, an electronic binder) that is delivered by email. The package is based on a standard work package for the type of activity to be performed, and includes the objectives of the work, work steps and instructions, a material list,
tool requirements, hours estimate, drawings, and outage scheduling information.
At the same time, the organization recognized the need for a work scheduling tool. It was felt that full-featured project management software is too complex and not a good fit for scheduling substation maintenance work. Again, in-house developers created a field crew scheduling tool. This tool has a “drag-and-drop” interface and is used to assign jobs to each crew one week in advance.
The tool is also used for timekeeping. This provides a mechanism to assess the relative efficiency of different crews and regions. This is provided in the form of metrics, e.g., to track estimates for a maintenance task versus actual costs and schedule adherence. The utility believes that this tool has reduced administration costs and increased maintenance productivity by approximately 40% where fully utilized.
At first, the new scheduling tool was not universally accepted. It took pressure from senior management to implement it across the organization.
This is a rare case in which an element of this program had to be driven from the top down.
Benefits of the work planning and scheduling tools: corrective action,
PM Implementation, continuing equipment reliability improvement)
Forced the organization to create standard work packages (Reference
[1], work planning)
Improved crew efficiency (Reference [1], work scheduling)
Provided feedback loops for continuous measurement of crew
efficiency (Reference [1], work measurement, employee performance)
Provided opportunity to understand and manage the response of
various parts of the organization to change (Reference [1], change
management)
Creation of the Maintenance Technical Library
Quite early, it was recognized that reliability engineers need data to determine how equipment is performing, but that time spent building queries to find data is time not spent making decisions. In-house software developers were used to create the Maintenance Technical Library, a sort of “wiki for equipment.” Reliability engineers,
maintenance area managers, and mechanics use this every day to find data from the CMMS, the data collection tool, and other repositories. It has pictures, specifications, drawings, and other documents for each component, tabs for preventative and corrective maintenance history,
instructions, and videos, which provide instructions and training on how to perform specific maintenance tasks.
Reporting Improves Visibility and Drives Corrective Maintenance
Standard reports with automated e-mail notification help maintenance personnel plan, schedule, execute, and track work. These reports are sent by email every day, and identify new work assigned in the last day,
“alarms” from the data collection tool within last the 24 hours, and overdue preventive maintenance work orders. All items requiring action are hyperlinked from the email to a management system where action can be taken. The utility reports that its employees respond better to this
“pushed information” than they do to a report that they must open and sift through to find actionable items. The reports help field supervisors keep well informed about what is going on in their area.
Benefits of the Maintenance Technical Library and daily reporting
Provides reliability engineers with easy access to equipment data
(Reference [1], business intelligence/analytics, reporting)
Allows field supervisors to see what is happening in their area and
act
Allows the entire organization to be engaged in asset management
processes. By providing visibility into asset information and
maintenance, even those who are not users of the CMMS are actively
engaged
Assessing Construction and Maintenance Resource Requirements
This utility was recently asked to explain how it knows that staffing is correct to do capital and maintenance work for transmission lines and substations. This question resulted in the realization that the company did not do much analysis of its backlog for 1 year, 2 years, 5 years,
etc. This led the company to develop a workforce model to forecast staffing levels.
The model includes preventative maintenance, corrective maintenance, and capital work. Preventative maintenance is relatively easy to forecast.
Labor for capital projects is based on the capital budgets for several years. The corrective maintenance forecast is prepared on a per-asset basis for major equipment, and includes consideration of the age and past performance of the asset. The corrective maintenance forecast does not rely on historical maintenance charges, as the company has found that historical maintenance costs are not a good predictor of future costs. Historical costs reflect past staffing levels, not equipment maintenance needs.
Performance against the forecast is assessed weekly. Some areas are more productive than others, and this helps show where process improvement or training may be needed. Equipment material condition in certain areas also affects performance against the labor plan. Thus far, the model has been fairly accurate based on predicted versus actual costs. The company plans further analysis to understand cases where actual costs have differed from the model.
Benefits of the staff level forecast model
Provides forecast of future staff levels for resource planning
(Reference [1], budgeting)
Provides a basis for determining optimal staffing based on employee
performance, training needs, work processes, and equipment condition
(Reference [1], work measurement)
Identifying Critical Assets and Tracking Activities and Performance
Documenting Maintenance Requirements and Tracking Activities
All equipment that requires preventive maintenance is represented by a record in the asset management system. The maintenance required for that equipment is documented there, as well as the source of the requirement
(regulatory commitment, code requirement, engineering judgment,
manufacturer’s recommendation, etc.)
Reliability engineers and administrators run reports periodically to verify that the required maintenance activities are being fulfilled by a preventative maintenance activity and to ensure that the work plans drive field personnel to do the maintenance tasks and collect data as specified by the equipment experts. The system does not automatically generate an “alarm” in the event there is a mismatch between the maintenance requirements and the implemented preventative maintenance activities.
Probabilistic Risk Model
This utility spent five years to create a sophisticated process to identify relative risk contribution to serve customer load for various equipment, including transformers, breakers, bus, capacitor banks,
relays, and disconnect switches. The tool uses a Monte Carlo simulation that includes a failure probability based on the asset health score
(discussed later) and the position of the equipment in the system.
Batteries were not included in the analysis because the failure of a battery does not in and of itself result in a loss of load.
Experience with this tool shows that due to the redundancy of system design, no specific primary transmission equipment is significantly more important than the rest with respect to serving customer load. Because of this, the current health of the equipment becomes the main influence on the risk attributed to specific equipment. This process did identify situations where protection systems could cause loss of load, and this drove more attention toward protection system replacements and upgrades.
These upgrades led to a significant reduction in risk calculated by the tool.
This process is used in system planning and design to look at the impact of proposed system changes. The company has a goal to run the analysis more frequently to identify the risk of working on certain assets considering the current system configuration. The current tool is not able to support that goal at this time.
Also, the model does not currently assign a financial benefit to risk reduction, so it does not yet calculate the capital investment necessary to achieve a certain level of risk. Nor does it allow the comparison of risk contribution of transmission assets versus distribution assets, and therefore cannot be used to apportion capital investment between transmission and distribution. These are opportunities to improve the model.
Benefits of the probabilistic risk model: identify assets requiring maintenance, long term planning)
Identifies equipment criticality
Uses criticality and health to determine risk (Reference [1],
equipment criticality, health algorithms, substation risk scoring)
Provides input to capital replacement strategy (Reference [1],
capital replacement program)
The system identifies high-risk assets and networks. Enhanced
maintenance, equipment replacement, and/or system design change are
risk reduction options (Reference [1], risk-based maintenance)
Corrective Action Program
When an event occurs, system operations enters the equipment affected,
type of outage, sequence of events, and the station affected and cascading effects into an incident reporting system. On-call engineers may be contacted to investigate. These reports are distributed in an email each morning and are discussed on the system operations morning call.
A reliability engineer places Information gathered from the reports and the morning call into a spreadsheet and presents that for review on a weekly screening call. The maintenance organization, system protection group, system protection field organization, system operations, and human performance improvement coordinator are invited to this call. An analysis level, apparent cause or root cause responsibility, and problem codes are assigned during this meeting. An internal operating experience site posts the results of the meeting. The reliability engineers prepare a weekly summary to record all investigative activities (test, repairs,
maintenance, and return-to-service requirements). Leadership support is important to the success of this meeting.
A monthly meeting sponsored by the Vice President of System Operations is conducted to review causal analysis performed on past events. The meeting covers the extent of condition reviews, determination of appropriate corrective action, and assignment of corrective action.
Effective tracking of corrective action plans has been challenging, as the plans are sometimes unclear and not actionable and there is no common work management system for everyone involved. Recently, the company has implemented a policy, modified the standard corrective action report template, and provided training to use SMART (specific,
measurable, actionable, realistic, and timely) corrective actions.
Effective cause analysis has also been a challenge.
The corrective action program also includes distribution of the North American Transmission Forum (NATF) Operating Experience Reports as appropriate, and the company is working to improve the process of submitting Operating Experience Reports to the NATF.
Benefits of the corrective action program
Documents all system disturbances
Specifies level of analysis required for each incident
Provides monthly meetings between system operations, maintenance,
and engineering to review incident analysis, recommend corrective
actions, review system performance, and assess system
vulnerabilities
Provides forum for tracking cause analyses and corrective action
plans (Reference [1], root cause analysis)
Drives continuous improvement
Equipment Performance Monitoring
On-line Monitoring
Despite this utility’s heavy reliance on condition-based maintenance,
the use of on-line monitors to gather condition data has been, until recently, limited. This situation was created by factors that might be considered differently by other utilities based on the size of the service area, divisions of labor and personal preference, and by the availability of other methods to collect data. Nevertheless, some of the lessons learned bear explanation.
Early in the development of the asset management program, this utility attempted to use a breaker monitoring system supplied with certain breakers to measure mechanism velocity, breaker timing, and gas density.
This effort was not successful. The monitor required an annual visit to download data. A high number of false alarms and required firmware upgrades also drove field visits, and failures in the power supplies of these devices caused grounds on the DC systems. In the end, the utility decided that the cost of maintaining and managing these devices was greater than the value derived from the data collected.
Out of this experience, this utility recognized the need to understand the total cost of managing monitoring devices versus the value and quality of the information provided. This experience shows that the full cost of maintaining monitoring equipment can easily be underestimated,
emphasizing the importance of fully assessing the value and reliability of a monitoring investment, perhaps on a trial basis, before committing significant resources.
Online monitoring can be justified in some cases. Monitors are used to drive condition-based maintenance decisions on tap-changers and transformers. This utility is in the process of installing on-line DGA monitors on all its transformers. In the future, transformer DGA monitor results will be integrated into the manual dissolved gas monitoring program to provide confidence that the monitors provide data consistent with manual samples. Currently, the two datasets are not connected and are not evaluated together.
Experience has shown that it is beneficial to track monitoring equipment as an asset in the CMMS so that maintenance and performance of the monitors can be tracked. This is helpful in assessing the cost effectiveness of the monitoring technology.
Capturing Data
This utility relies heavily on inspection and maintenance data in the CMMS to track failure rates, leak trends, and unfavorable corrective maintenance trends.
The utility also tracks interrupting device operations in the historian.
This data is used to automatically create alerts showing which devices need to operate to meet annual exercise goals. In the future, the historian and CMMS may be connected to automatically create exercising work orders.
Equipment Reliability Reports and Maintenance Effectiveness
Reports are created to measure effectiveness of the maintenance program by equipment type (average and total maintenance hours, for example).
This is used to adjust maintenance program activities and could be used to monitor the performance of the various parts of the maintenance organization, although such monitoring of organizational performance is not fully implemented.
Periodically, reliability engineers and peer teams create maintenance effectiveness reports. The peer team determines the metrics for the asset types under their control. Among other things, the report shows maintenance costs and trends, reliability, average equipment age, and common issues. The report also identifies recent improvements in the maintenance program, such as new test equipment, processes,
instructions, etc.
Benefits of the equipment reliability and maintenance effectiveness reports
Provide objective measures of program success (Reference [1],
reporting)
This utility assigns equipment health scores through scorecards created in Excel using a set of attributes for each asset type. The scorecards are used primarily to develop priorities for capital replacement. Field personnel and equipment engineers on the equipment peer team assign weighting factors to the attributes. The factors are designed to match organizational priorities (such as SF6 leak rates on high voltage breakers). Typical attributes include the number of corrective maintenance hours required, number of operations, number of fault operations, hotspot data, etc. Scorecard data entry is manual.
There are scorecards for circuit breakers, disconnect switches, circuit switchers, transformers, batteries, and high voltage test sets. It is challenging to keep these scorecards up-to-date due to the lack of automation. Replacement priorities for this equipment are still discussed as part of the peer team process, even if a scorecard has not been updated.
In the future, the utility hopes to automate certain scorecard inputs by pulling data from the CMMS, the data collection tool, the historian, the SF6 leak monitoring application, the transformer DGA database, the hot spot tracking tool, and financial tools.
Transformer End-of-Life Modeling
The capital budgeting process provides a budget for transformer replacement. The transformer equipment subject-matter expert establishes replacement priority based on the transformer scorecards. Inputs to that scorecard include DGA results (day-to-day assessment) and results of EPRI PTX[2] analysis. This company fed 40 years of DGA data into PTX and compared PTX failure predictions to failure history to refine and validate the predictive accuracy of the model. Proximity of the transformer to bodies of water is also considered.
Benefits of the equipment scorecards and transformer end-of-life modeling
Provide objective measures of equipment health (Reference [1],
health algorithms)
Provide information for capital project decision-making (Reference
[1], capital replacement program)
Equipment Nameplate and Baseline Installation Data
When the asset management program was in its infancy, there was little nameplate information in the databases. In other words, it was not clear what was installed. There is an on-going effort to improve the quality of the information in the asset databases by using preventative maintenance work orders to collect information and temporary help to take pictures of nameplates.
Since preventative maintenance tasks are driven by nameplate information, the CMMS is configured such that preventative maintenance work orders cannot be executed until enough asset specification information is entered to allow the system to select the correct job plan for the preventative maintenance.
For new equipment, there is a job plan that gathers the required information at installation. There is now a heightened awareness of the importance of nameplate data that has helped improve the equipment database.
In addition to nameplate data, baseline information needs to be collected for certain equipment (battery cell resistance, for instance).
This is collected through job-plans in the data collection tool or by recording data in a preventative maintenance work order that is opened at the time of installation specifically for this purpose. There is room for improvement in collecting and storing most new equipment acceptance testing and inspection information.
Engineering Work Management System
The utility is in the process of creating a work management system to support engineering resource planning for capital projects and improve engineering schedule adherence. This system is applicable to asset management, because the ability of the company’s engineering function to perform projects is another kind of asset and impacts what capital replacement the asset management function can be expected to complete.
The tool supports a workflow that starts with an engineering service request. Some of these service requests generate a project, which is linked to the group that will install the asset. This allows the construction dates to be determined, which in turn drives the engineering schedule. The system tracks engineering deliverables,
budgets, time charging, and engineering resource loading.
The capital project portfolio is optimized by scoring potential projects after they are estimated. This goes on at the corporate level according to a set of corporate drivers. The asset management function establishes priority for projects to ensure that the desired projects make the budget cut.
Chapter References
“Transmission Substation Asset Management – A Top-Down Approach”
(See Chapter 8)
Power Transformer Expert Systems Software Version 7.0. EPRI, Palo
Alto, CA: 2020. 3002019258
This chapter presents an example of how an electric utility in the Midwest United States implemented a transmission line and substation asset management program over a six-year period. The study focuses on the implementation and development of the organizational structure,
people, and tools used in the program to realize value from the organization’s assets. The focus of this chapter is on substations, even though transmission line assets were included in the program. Most of the principles presented here apply to other types of assets.
This utility does not have a formal ISO 5500x program but has attempted to realize the benefits of a program as documented in ISO 55001 and ISO
55002.
The author of this chapter hopes to achieve three objectives:
First, provide specific information about “how” to implement an
asset management program and culture by sharing one utility’s
journey. There are many documents that provide a “framework” for
formal program implementation, e.g., ISO 5500x and EPRI’s technical
report *Asset Management Guidelines Development (*Reference [1]).
These documents are technically strong, explain “why” a company
would want to develop a formal program, and provide some of the
requirements for such a program, but they purposely do not provide
details on how to proceed.
Second, answer questions commonly asked by other utilities when they
inquire about the current program. This utility has shared practices
with many utilities over the author’s seven years in the
transmission and substation business. There are six to eight common
questions that everyone seeks answers for. Additionally, there are
certain tools and programs that other asset managers always want to
learn about.
Finally, stress important things that people tend to not think about
when implementing an asset management program. For example, many
people jump straight to the technical aspects of the program and
spend little time gaining executive sponsorship or using a formal
change management process. Asset management impacts many other areas
of the organization, and attention to these elements is critical for
a smooth rollout and long-term success.
The chapter identifies program elements and discusses when and how each element was addressed during the implementation. Additionally, the case study references specific elements in Reference [1]. The reader should remember that implementations vary depending on organizational structure and maturity.
Text in this format identifies when the accompanying element should be implemented. It may or may not represent how the element was implemented at this utility.
Overview and History of the Program
Like many utilities in the United States, this utility’s transmission line and substation departments had no formal asset management before the development of this program. Maintenance activities were not planned in detail, work was scheduled by the supervisors and lead craft, and no thought was given to moving to an asset management culture. The substation work was corrective maintenance, time-based preventative maintenance, and a few condition-based maintenance tasks.
Senior management leadership decided to duplicate the successful asset management program already implemented in the generation side of the organization. Therefore, the development of this program could be described as “top-down.” The new program would focus on:
Developing a formal asset management program
Formal maintenance planning and scheduling program (with emphasis on
work execution)
An improved condition-based maintenance strategy
The substation North American Electric Reliability Corporation
(NERC) compliance program
Organizational Structure
The organizational chart (Figure 1) shows where the asset management program fits into this organization. Note that asset management does not report through the director of maintenance. This is important.
Maintenance is naturally oriented towards correcting problems now, which can pull asset managers away from the important task of planning for the future. Asset managers are the fire marshals, not the fire fighters.
Figure 9-1 : Organizational chart showing the position of asset management
The utility took a top-down approach to implement the asset management program. Executive leadership was involved and approved the strategic plan for the program. Executive support greatly increases the potential for long-term success for the program by ensuring access to:
Funding – A successful asset management program needs dedicated
personnel, training, access to consulting services (if used), access
to asset management tools and software, and support for process
development.
Culture – An asset management culture requires relationships with
many other departments in the organization. It needs to be part of
the organization’s core values and expectations. Executive
leadership ensures that all departments understand that they have a
role in asset management and are responsible for the success of the
program.
Change Management –To create a successful asset management culture,
the message for change needs to come from the top and be delivered
down the organization through employee supervisors. Executive
leadership should communicate clearly and often throughout the
program development.
The Reliability Model
The asset management program was built around the reliability model represented by Figure 9-2 and Figure 9-3. The stages (walls of the transformer in Figure 9-2) and elements (foundation of the substation in Figure 9-3) model a system that supports a culture of asset reliability improvement. The case study uses this model to show one example of how to implement an asset management strategy.
Figure 9-2 : Reliability model: stages
Figure 9-3 : Reliability model: Elements
Stage 1 – Plan (Design the Program)
The foundation of the asset management program rests on the program plan. The elements of the plan are shown in the first two layers of the foundation of Figure 9-3.
Mission and Values
Program vision and values are developed to aligned with corporate mission, vision, and values. These are developed by the leadership as well as the program champion. Example vision, mission and value statements are:
Vision: We will achieve first quartile safety and reliability
while maintaining investor returns by following reliability
engineering principles and providing superior customer support using
innovative partnerships.
Mission: We use industry best practices and innovative solutions
to improve safety, maximize reliability, and increase investor
returns. We will manage assets to realize value through managing
risk and opportunity, in order to achieve the desired balance of
cost, risk, and performance.
Values: People, Safety, Service, Innovation, Communication,
Integrity, Dependability, Teamwork, Fun, Productivity, and Employee
Development
Management Commitment
Leadership must support the program. Leaders deliver the message of support with passion and consistency and deliver it often. The program needs to be positioned as a “must” and not labeled an initiative.
Initiatives have endings. There must be a goal to build a sustaining culture (Reference [1], Section 3, “Model Input/Output Definitions,”
D1).
When: If building a top-down program, the mission must be established,
and management commitment obtained, before the program is rolled out
(early year 1).
Departmental Partnerships
Asset management touches most parts of the organization. Everyone in the organization must understand their role in asset management and how they can affect the program. Once there is management commitment across all affected areas of the organization, partnerships can be formed. Figure
9-4 shows partnerships formed within the subject utility, along with the primary interactions with the partner departments.
When: Partnerships are formed during the management commitment phase of the program (early year 1).
Figure 9-4 : Departmental partnerships
Organizational Structure
An effective organizational structure supports the strategic plan and promotes the asset management culture. Organizational structures should align with work processes to drive a proactive mindset. Maximum asset reliability is achieved when the organization provides:
Engineering staff dedicated to equipment reliability
Personnel for work planning and scheduling
Supervision of work execution
As shown in Figure 1, this company separated the reliability engineers and the planning and scheduling function from work execution. The reliability engineers, as they are not directly responsible for maintenance work execution, are invested in improving equipment and system performance, not in performing specific maintenance tasks. Field supervisors, free of scheduling and resource assignment tasks, spend more time supervising the work.
When: Organizational structure should be defined early in year 1.
Change Management
Just like any major program rollout, a change management process and team are needed to ensure smooth and consistent program deployment plan.
There are many change management methodologies to choose from. This utility used a process provided by a change management consultant for the asset management program implementation. There are other such process tools that appear to be just as effective.
When: Change management should occur immediately after management commitment has been achieved (early year 1).
Gap Analysis
The utility completed two gap analyses. The first was an internal analysis (also known as a status assessment) using a model from a consultant. The internal gap analysis focused on five pillars and 29
interdependent elements. Immediately after the internal gap analysis was complete, an independent consultant was hired to perform a second gap analysis against the ISO 5500x standard. The two analyses provided similar results. The ISO 5500x analysis did show more weakness in the management system, but this is to be expected, as the ISO 5500x model requires a formal asset management system.
When: A gap analysis should be performed early in the plan stage (early year 1).
Define Roles
For the program to be successful there should be a shared belief and understanding that everyone in the organization has the responsibility to support good asset management principles. This is established by defining the processes, roles, and responsibilities that everyone follows.
At this utility, documented roles and responsibilities were developed for all positions inside the executive director’s organization. A RACI
(responsible, accountable, consulted, informed) responsibility matrix was created with all partnerships shown on Figure 4 to ensure responsibilities were understood.
When: Processes, roles, and responsibilities should be established in year 1.
Goal Development
Program objectives and goals need to be developed and approved by leadership. This provides the basis for implementing the program as envisioned. Program goals should align with corporate goals and support the purpose of the asset management program, which is to realize value through managing risk and opportunity, to achieve the desired balance of cost, risk, and performance (Reference [1], Section 3, “Goals and Policies”).
When: Objectives and goals should be developed early in year 1.
Program Roadmap
The program roadmap shows how the program will develop. The roadmap should list development activities, have milestones, and assign responsibilities to the partner departments. This utility decided that a five-year roadmap was appropriate based on the gap analysis and available resources (Reference [1], Section 3, “Goals and Policies”).
When: The roadmap should be developed in year 1.
Supervision
Supervisors should focus on ensuring that their employees are engaged,
ensuring work is completed with high precision, ensuring efficient and effective work practices, and ensuring work is completed safely. Most people trust their direct supervisors, and therefore it is important that communication and change is driven through supervision. When implementing an asset management culture, supervisors must:
Be involved in program planning
Be “all-in” with the program plan
Understand and be able to speak to all aspects of the plan
Understand the roles and responsibilities of all groups with which
they interact
When: Involve supervision early in year 1.
Employee Engagement
Create a climate that promotes excellence and supports every employee’s role in the asset management culture. For this utility, reliability was added to the corporate mission statement. Yearly reliability summits were held with members from all departments to improve understanding of the components of the asset management program. It was made clear that the program was supported and driven by top management, who expected employees to support it. As the program matures, such engagement meetings may transition from educating employees and building the culture to communicating successes and planning future program development.
When: Efforts to engage employees should begin early in year 1.
Employee Performance Management
Performance management is the process by which management holds employees accountable for doing things right. Progress against the program roadmap should be tied to performance incentives for all departments involved in the program. Program goals should be reflected in individual employee goals, including those who work in the partner departments. At this utility, goals were created to support the program and rolled down to employees. Employees had the ability to modify the goals so that they could personalize them based on their impact.
When: Employee performance management should begin early in year 1.
Stage 2 – Do (Deploy the Program)
Work Planning and Control
Work planning and control focuses on how work is managed and documented,
including work identification, planning, prioritization, execution, and finish comments (recording of work history). An effective work management system improves the efficiency and effectiveness of field personnel.
This utility developed work control processes to ensure all steps needed for successful work execution are identified. Work is made up from a ready backlog, which is 4-6 weeks of work that has been fully planned and total backlog (all planned and unplanned work). Work is considered planned when parts are ordered and staged (if appropriate),
documentation is provided (precision maintenance procedures, work instructions, drawings, manuals, and any other documentation) and additional details that can be done in advance are complete.
When: Depending on the maturity of the organization, size of the department, and number of resources, work planning should start sometime in year 1. It would not be unusual for this to take 8-12 months to complete, as it involves process development and formal training. Begin this in year 1 and finish within a year.
Work Scheduling
Scheduling is sometimes considered as a part of work planning and control. The author has seen instances where this function occurs outside the maintenance organization (such as in engineering). As shown in Figure 1, the scheduling function of this utility is handled by the asset management organization, which is part of maintenance. This ensures that the priorities of the maintenance organization are aligned with the goals of the asset management function.
At this utility, work is scheduled out 4 weeks (100% scheduled one week out, 80% scheduled two weeks out, 60% scheduled three weeks out and 40%
scheduled four weeks out). Schedulers obtain clearances and coordinate with internal and external customers as needed.
When: Depending on the maturity of the organization, size of the department, and number of resources, work scheduling should be started sometime in year 1. Scheduling processes seem to be easier to develop and implement; therefore, this can take 2-4 months to complete.
Operator Care
For a utility, operator care means how the system is operated. For example, written procedures for switching or re-energizing substation assets should consider how the procedure affects not only safety or system operations, but also equipment health and reliability. At this utility, the asset management team is involved in developing operating procedures and, through participation in root cause and human performance event analysis, identifying how the actions of operators affect equipment health (Reference [1], Section 4, “Maintenance Process Diagram” and Figure 1).
When: Year 2 or concurrently with year 1 elements, depending on staff levels and competing work.
Equipment and System Design
Studies have shown that 85% of the total life cycle cost of an asset is determined during the design phase (Reference[2], Figure 1). The asset management team has the best chance of maximizing inherent reliability when it works with the engineering team to develop good equipment standards and specifications.
When: Involve reliability engineers in the standards and specification development process in year 2 or concurrently with year 1 elements,
depending on staff levels and competing work.
Procurement
Most procurement teams are naturally interested in buying low-cost assets and ensuring timely delivery. The asset management team should complete life cycle cost (LCC) studies on critical asset classes to demonstrate the total cost of ownership for key asset classes. This utility started with transformers, breakers, batteries, and relays. LCC analyses were shared with procurement personnel so that total life cost was incorporated into proposal review criteria.
When: Year 2 or 3, depending on maturity.
Materials Management
A good materials management system ensures the right materials are available, at the right time, and in the right place. The asset management team should complete a reliability audit to ensure assets are properly stored and maintained while in inventory.
When: Audit the materials management system in year 3.
Computerized Maintenance Management System
The computerized maintenance management system (CMMS) is the most important tool of the maintenance and asset management groups. Consider the following when selecting and configuring the CMMS:
Think with the end in mind! What outputs do you want from the
system? Who is going to maintain the system (keep data updated)?
Visit other utilities, look at their configuration, and collect
lessons learned.
Consider the ability of the system to support customizable fields to
classify work orders.
Create process flows before building the system.
Recognize that CMMS are expensive and labor intensive to implement.
Be prepared to complete the work.
Train everyone that will use the system.
Good problem coding in the CMMS helps reliability engineers with
“bad actor identification” as well as when benchmarking other
companies. The North American Transmission Forum(NATF) has developed
a problem coding system that provides guidance for building coding
into the CMMS system.
If implementing a new CMMS, the author strongly recommends gaining an understanding of ISO 14224:2016.
At this utility, much effort went into implementing this system. CMMS implementation started in mid-2011 before the formal asset management program was developed. The CMMS used by this utility can store equipment criticality scores and allows users to build health algorithms to calculate equipment health scores. This gives the utility the ability to calculate risk scores (Reference [1], Section 3, “Model Input/Output Definitions,” D8).
Training
Initial and ongoing training at all levels is essential to the success of any organization. There is no sense in trying to drive asset reliability to a higher level if field personnel do not have the proper training to maintain and repair equipment. Formal training programs should be reviewed frequently and improved upon as new technological advances in equipment are introduced into the system.
When: Training is a continuous process.
Work Measurement
Accurate labor estimates are an essential part of the planning and scheduling process. Understanding how long it takes to complete a task improves scheduling accuracy and craft efficiencies.
This utility used measured work hours to determine the average time to complete various tasks. This was used to establish the standard times for the tasks (Reference [1], Section 5, “Performance Metrics”).
When: As soon as there is a system in place to measure work.
Loss Prevention
The asset management team should work closely with the loss prevention group. A mature and advanced asset management program can result in reduced insurance rates for the utility. Insurers are concerned about risk, and a good asset management program strives to achieve the lowest cost of ownership while balancing risk.
At this utility, dissolved gas analysis (DGA) information and health and risk scores for various equipment are made available to the loss prevention group and insurance providers. Insurance providers also have access to information about maintenance activity exceptions, such as a missed activity. This has had a favorable effect on rates.
When: Year 3 to 4.
Budgeting
The primary purpose of the asset management team is to extract the most value from the organization’s assets with the lowest risk. Reliability engineers are involved in the budget development process. Tools such as preventative maintenance and reliability centered maintenance can reduce maintenance budgets by improving efficiency, effectiveness, and reliability. The asset management team makes budget projections more accurate using analytics to better understand historical task times and cost. This information is used to forecast the cost of upcoming preventative maintenance. Reliability engineers work to replace equipment or perform preventative maintenance tasks at the optimum time and not after catastrophic failures. It has been shown that reactive maintenance can cost much more than planned and scheduled work
(Reference [3, p. 48].
When: Start year 1 and grow as the program does.
Reporting
“What gets measured gets improved.” The asset management team must develop reporting for budgeting, work execution, and reliability results to determine if the program is succeeding. Good reporting requires good data and easy access to it, so this requires a relationship with the information technology department.
When: Start developing reports in year 1 and grow as the program does.
Business Intelligence and Analytics
The availability of data and power of analytical tools is changing the way utilities view and act on data. Here are examples of how this utility is leveraging data:
Battery health dashboard – Used to identify failed cells or identify
incomplete test. Provides the capital replacement list for planning.
Test data comes directly out of the CMMS.
Transformer health dashboard – Used to identify transformers by risk
and view the health of transformers. Provides a list of transformers
that have high PCBs. Identifies transformers that need simple
moisture removal filtration, oil processing, or oil replacement.
Used to view comparative results from various transformer tools such
as EPRI PTX, IEEE DGA condition codes and internal algorithms. Helps
subject matter experts review DGA results in a better format. Test
data comes from the CMMS, PTX and the utility’s oil lab.
Bad actor tool – Used to identify bad actors and analyze equipment
by work order cost, work order count, manufacturer, model, type, or
any field in the CMMS system.
Maintenance schedule compliance.
Operating and maintenance actual cost versus budget.
There are many tools used by asset management and reliability professionals to help them achieve the maximum value from their assets.
The remainder of the discussion of this stage covers tools and programs that are used to support the asset management culture and derive maximum value from equipment assets.
Condition-Based Maintenance and Predictive Maintenance
This utility leveraged as many predictive maintenance (PdM) and condition-based maintenance (CDM) activities as possible. Non-intrusive testing helps reduce human error and provides early detection of defects. For a successful program, field personnel should have formal training and testing standards. Document all cost savings. Programs currently used by this utility are: infrared thermography, insulation testing, SFRA testing, oil condition testing, DGA testing, partial discharge testing, ultrasonic testing, first trip testing, corona testing, SF6 leak detection testing, and radio noise testing (Reference
[1], Section 3, “Model Process Definitions,” P7).
When: Program enhancements for CBM and PdM began year 1.
Preventative Maintenance Optimization
Most utilities have preventative maintenance procedures and standard frequencies for maintaining assets. Many preventative maintenance frequencies are originally set based on manufacturer recommendations.
These tend to be inappropriate, as manufacturers may not understand the context in which the equipment operates.
Preventative maintenance optimization (PMO) is a method for reviewing and modifying existing preventative maintenance to improve the efficiency and effectiveness of maintenance task. At a high level, one can think of PMO as a streamlined reliability centered maintenance (RCM)
that is worked in reverse. In other words, while RCM seeks to identify the ways that equipment can fail and select maintenance activities to address the causes of those failures, PMO assesses the existing preventative maintenance tasks, identifies the failure modes that these tasks address, assesses the failures that are not being prevented, and identifies proactive maintenance tasks that can be used to prevent those failures.
The author believes that PMO is more effective than RCM for utilities that already have fully developed preventative maintenance plans. With flat or declining operating and maintenance budgets, the PMO effort should be addressed early in the asset management program implementation. Benefits include reduced labor, reduced material cost,
reduced maintenance, repair, and operations inventory, and reduced total cost of maintenance due to increased reliability.
This utility underwent an extensive PMO effort that lead to annual savings of just over 7% of the maintenance budget. When deciding what assets to start with, consider equipment criticality, equipment bad actors, and equipment counts. Be sure to document any changes, follow your change management process, and document cost savings (Reference
[1], Section 3, “Model Process Definitions,” P5; Section 4,
“Maintenance Process Diagram;” and Figure 1).
When: Initial preventative maintenance optimization was completed in year 3.
Reliability Centered Maintenance
Reliability centered maintenance (RCM) is a process that helps determine the appropriate maintenance strategies for a utility’s assets. The four primary objectives are to preserve the function of the system, identify failure modes that could impact the system, prioritize those failure modes, and select the most effective task to mitigate or reduce the likelihood of those failures.
RCM analysis is a labor and time intensive process, and utilities may decide not to complete it on all systems. This utility completed an RCM analysis on all the substations that have a direct tie to a nuclear power plant. Given the criticality of these substations and the impact they could have on the generation facility, the utility decided that a complete RCM analysis was appropriate (Reference [1], Section 3,
“Model Process Definitions,” P5).
When: RCM analysis (limited scope) was completed before the asset management program was started.
Precision Maintenance Procedures
Precision maintenance is a necessity for organizations that strive to be world class and provides real impact to increased reliability and reduced O&M cost.
This utility decided to create precision maintenance procedures while working on preventative maintenance optimization. A technical writer was hired to complete maintenance procedures as preventative maintenance plans for assets were being reviewed and modified. A workflow was incorporated to ensure that maintenance supervisors, field personnel,
and the equipment subject matter experts provided input to procedure development. These procedures were attached to the assets in the CMMS and are issued as part of the planning package (Reference [1], Section
3, “Model Process Definitions,” P5).
When: Precision maintenance procedure development started in year 3.
Root Cause Analysis
Root cause analysis (RCA) is a systematic process for identifying the root causes of problems in equipment, processes, or procedures. Also called defect elimination, RCA is a very essential part of a good reliability program. Development of an RCA procedure or guideline is essential in defect elimination and continuous improvement. There are many different RCA methodologies to choose from and utilities should carefully choose the method that best fits their needs. This investigation should include discussions with peers. Many of the vendors offer software that helps facilitate, document, track and audit the process.
The RCA document should contain the following key elements: purpose,
description, scope, definitions, allowable RCA methods, roles and responsibilities, incident triggering, RCA process timeframe with milestones, reporting requirements, and auditing requirements.
This utility selected a program that was used in other areas of the company and on which some of the reliability engineers were already trained.
When: Start RCA process document and method/vendor selection in year 1.
Equipment Bad Actor Identification
Identifying problem equipment provides a good opportunity to reduce reoccurring emergent work, improve system reliability, and maximize the value obtained from the utility’s assets. Bad actor identification requires identifying problem equipment by analyzing and ranking assets by the total cost of corrective maintenance, work order count, or a combination of both. Once a bad actor is identified, reliability engineers can determine the root cause of failures and implement a plan to correct the situation. The bad actor program relies on data in the CMMS. Corrective work orders should be written and coded to each specific asset, which means that blanket work orders should be avoided.
Good work order coding makes problem identification efficient and effective. If reliability engineers must rely on searching finish comment for specifics, the effectiveness of the program can be seriously hampered. There should be a process for coding work and field personnel should be trained on the process. Ensure craft employees understand the importance of coding and finish comments.
If the CMMS does not support work order coding, there should be expectations and training on how to complete work order finish comments
(Reference [1], Section 3, “Model Process Definitions,” P3).
When: Started bad actor identification in year 2.
Equipment Criticality
When the roadmap was developed, the utility decided to employ risk-based strategies. Criticality scores were developed for all substation and transmission assets. Criticality determines the consequence of a failure and is required to calculate risk scores. It also is used with large capital replacement decisions, economic replacement models, planning and scheduling decisions, and reliability analysis decisions, including RCM and bad actor analysis selections. The steps used to obtain the criticality scores are:
Assemble cross functional team.
Train the team and begin model development.
Determine what criticality criteria apply.
Develop and define the criteria.
Rank criteria in order of importance.
Develop criteria weight values.
Run a sample analysis and complete a sanity check.
Complete the analysis.
For this utility, the criticality score has the following criteria:
Regulatory and environmental, including nuclear impact
Safety
When: Criticality analysis was completed in year 2.
Health Algorithms
Health algorithms were developed for major asset classes. Transformers,
breakers, batteries, and relays received the primary focus. Health refers to the probability of failure of an asset. Assets in poor health negatively impact the health score. Health algorithms are used in risk-based maintenance, planning and scheduling decisions, and reliability analysis decisions (Reference [1], Section 3, “Model Process Definitions,” P3).
A utility might consider the following factors in a health algorithm for transformers:
Design or manufacturer
Bushings with known issues
Transformers with known issues (manufacturer rating, manufacturer,
and vintage). Use specific failure rates when possible.
Shell vs. core vs. rectangular
Winding material (copper vs. aluminum)
Operational stress
Failure rates (age can be substituted if you do not have access to
failure rates)
Consider through-fault currents if possible
Transformer loading
Maintenance history
DGA results
Electrical insulation test results
Oil condition results
IR results
Work order count
When: Health algorithm development began near the end of year 1 and
is an ongoing process. The bulk of the algorithms were developed in
year 2 and year 3.
Risk-Based Maintenance
Risk-based maintenance (RBM) is a method for economical use of maintenance resources that gives priority to assets that carry the most failure risk. A risk score is calculated by determining the probability of failure and multiplying that by consequence of the failure.
Maintenance activities are determined based on the risk of the failure.
At this utility, risk scores are used by schedulers to establish maintenance priority. In addition, intervals for certain monitoring and maintenance, such as transformer DGA, may be reduced on high-risk assets(Reference [1], Section 3, “Model Process Definitions,” P5).
When: RBM started after risk and criticality were completed.
Substation Risk Scoring
Since the utility is already getting risk scores for major assets classes from the CMMS, it is now leveraging that data, along with additional data, to rank entire substations by risk. This is a holistic view that considers major equipment risk scores, the impact of the substation to system reliability, total operating and maintenance cost,
ground grid, fencing, construction type, and safety considerations, such as approach distance. It is used to drive reliability improvement projects and feed data to the major construction capital database tool.
This helps identify whether there is a need for whole station replacement or targeted asset replacement(Reference [1], Section 3,
“Model Process Definitions,” P4; “Model Decision Point Definitions,”
Q3).
When: Substation risk scoring was started in year five.
Economic Modeling Tool
This tool focuses on large capital replacement assets such as transformers and breakers. It has three primary purposes.
Identify the appropriate number of spares required considering cost
versus risk
Provide a rank-order replacement list for major equipment
considering the remaining economic value and probability of failure
Show the remaining economic value of assets to assist in repair /
replace decisions.
This utility used a consultant to help with the development of an economic model for transformers that leverages the risk scores already developed for the maintenance strategy (Reference [1], Section 3,
“Model Process Definitions,” P4, P7, and P8).
When: The modeling tool was developed late in year 5.
Capital Replacement Program
Ensuring that the correct capital projects are implemented is critical to a utility’s continued success. With limited budget and stakeholder pressure, utilities should have a good process for selecting capital replacement projects. Capital replacement projects originate from two areas for this utility’s substation department.
The first source of projects is the director of substation maintenance.
The director’s team has blanket work orders for small capital projects
(breaker replacements, relay upgrades, battery replacements, etc.) which are initiated by maintenance engineers. These projects are driven by bad actor identification, obsolescence of spare parts, or other reliability reasons. For these projects, the asset management team develops the replacement list based on economic and statistical modeling. This ensures that assets are replaced according to agreed-to methods and not by “gut feel” or perception of the needs of one work region over another.
The second source is larger projects, such as an entire substation replacement or transformer replacement. A major construction team is responsible for such projects. That team has a tool to score projects in order of importance. The asset management team provides inputs to that model to impact scoring based on reliability and economic data. For example, transformers that are nearing the end of their economic life are entered in the model, so these transformers are considered and planned for replacement. Health and criticality scores impact transmission line or substation replacement (Reference [1], Section 3,
“Model Process Definitions,” P6).
When: Started year 5.
Reliability Engineering Team
The reliability engineering function is responsible for adhering to the program roadmap throughout the entire life cycle of assets. They rely on many tools and methods. The author receives many questions around the make-up of the team, training, tools, certifications, and education.
Additional details are provided in Section0.
When: Starting year 1.
Stage 3 – Check (Review the Program)
Audit
Audits ensure that everyone is complying with the processes that have been implemented. They also provide the opportunity to identify areas for improvement. The asset management team should complete audits on any process, standard, or controls that are developed to support the asset management culture. Auditing requirements are built-in to individual elements of the program, such as root cause analysis.
When: Starting in year 3 or as needed.
Monitor/Analyze
Business intelligence tools are used to look at common industry metrics,
such as SAIDI and SAIFI, and the contribution of substations to those metrics. This provides a “big picture” assessment of program effectiveness. Operating and maintenance costs are monitored through targets for proactive maintenance versus corrective maintenance, which is reactive.
Benchmark
This utility participates in an annual cost, reliability, and safety benchmarking survey offered by a consulting firm. The utility also participates in the EPRI Industry Equipment Database, makes use of NATF surveys, and is heavily involved in the NATF Asset Management Practice Group.
Stage 4 – Act (Improve the Program)
Improvement Plans
The utility actively assesses which assets are creating the greatest reliability and cost impacts and pursues maintenance techniques,
technologies, and capital plans to improve performance. For example, in recent years, substations batteries were an area of focus, with special attention paid to condition monitoring and replacements. More recently,
improved monitoring, new test and inspection capabilities, and a health index for distribution breakers have been added to the program.
Adaptability
Over the years since the asset management program was created, the asset management group has expanded its capabilities and modified its scope as the company learned. As corporate goals and needs changed, such as through operating and maintenance cost reduction targets, while the asset management program has adapted, the program has continued to pursue a risk-based approach while clearly communicating risk to leadership and adjusting risk tolerance where warranted.
Building the Asset Management Team
Based on the strategic plan, this utility decided that one manager and four reliability engineers would be required for the asset management group (Figure 1). The team of reliability engineers consisted of two electrical engineers, one mechanical engineer and one industrial engineer. However, specific engineering disciplines were not a primary concern when selecting people for the position of reliability engineer.
The author has learned from experience that people skills are more important to the success of the engineer and program than specific technical skills. Reliability engineers need to collaborate with everyone from field workers to executives, manage internal and external customers, and demonstrate passion and leadership. Most tools used by reliability engineers require additional training outside of college. It is more important to be collaborative and approachable than to possess specific education. The author has hired non-degreed people for such a role and had great success.
The team was hired over the period of about 6 months. Reliability engineers are stationed in two regions and are assigned to help field crews throughout their region. They are also responsible for educating field crews, supervisors, and others about the program.
All reliability engineers receive some form of training on the following:
Root cause analysis
Risk management
Reliability centered maintenance
Preventative maintenance optimization
Life cycle costing and economic modeling
Equipment criticality modeling
Building health indices
Risk-based maintenance
Statistical modeling
Equipment specific training
PdM and CBM technology training
Reliability engineers are encouraged to take leadership training annually.
While the passing of institutional knowledge through on-the-job exposure is critical to the development of reliability engineers, this does not take the place of formal training. The training methods used by this utility include:
Classroom
Internal book clubs
Online training
Field training on specific equipment
Factory visits on specific equipment
All reliability engineers are required to obtain one of the following certifications within 5 years:
Certified Maintenance and Reliability Professional (CMRP)
Certified Reliability Leader (CRL)
Certified Reliability Engineer (CRE)
Every reliability engineer is assigned to participate in activities of North American Transmission Forum (NATF), Electric Power Research Institute (EPRI), Center for Energy Advancement through Technical Innovation (CEATI), or in activities sponsored by an asset management consultant. This helps the engineer stay current on industry practices.
P. Barringer, “Life Cycle Cost Analysis—Who Does What?,” in
National Petrochemical & Refiners Association Maintenance
Conference and Exhibition, San Antonio, 2004.
R. Gulati , Maintenance & Reliability Best Practices, New York:
Industrial Press, Inc., 2012.
1.10 - Chapter 10 - Conclusions and Recommendations for Additional Work
Conclusions
With the successful development and acceptance of a number of EPRI analytical tools and equipment performance databases and the increased appreciation of asset management principles for guiding power delivery organizations, it was deemed appropriate to revisit EPRI’s Power Delivery Asset Management (PDAM) Guidelines. Working with utility advisors, the objective was identifying any additions required to make certain that the latest version fully addresses current utility needs.
This report documents the results of the work to produce an updated guide, and includes two utility application examples, presenting detailed descriptions of two different approaches to developing transmission substation asset management programs and some lessons learned from their implementations.
Recommendations
In the continuation of this effort, interested members are invited to join in additional reviews of the guide to help identify gaps and develop a priority list of any identified guideline needs. In addition to any result from the utility review, areas for potential further development identified include:
Better performance metrics for transmission equipment
A more detailed replacement process model to parallel the
maintenance process model presented here
Incorporation of EPRI’s decision support analytics, such as CBRR and
spares strategy evaluation methodology into PDAM
Integration of industry-wide databases into the appropriate PDAM
processes
Additional utility implementation examples to further illustrate
PDAM concepts and present lessons learned
Building on the existing guidelines, using this report as a platform and working with members, EPRI will produce a new, updated PDAM guide as needs are identified.
