Overview & Events

Research Value

• More effective use of existing infrastructure and data
• Early identification of type issues, reducing unplanned outages.
• Improved reliability and availability using analyses based on actual asset health and risk to determine maintenance actions.
• Reduced reliance on time-based maintenance
• Improved capital planning decisions based on industrywide equipment performance and failure data.
• Gain more accurate and timely knowledge about asset condition and life expectancy

Member Benefits

• Offer guidelines on what equipment specific data to collect and why?
• Provide algorithms
  • To assess the condition of in-service assets and identify assets at risk
  • To initiate triggers and alarms for automated maintenance actions.
  • To assist utilities with repair, replace and/or refurbish decisions
• Provide information based on industry wide equipment performance and failure data to help utilities in making better informed decisions. For example,
  • Capital planning and spare strategies
  • Maintenance program development; task and timing selection
  • Specification and selection of new assets
  • Benchmarking
• Provide consistent analytical basis for making capital and O&M decisions

At A Glance

Aging equipment fleets, more stringent operating requirements, financial constraints, and lost expertise through retirements make managing substation assets challenging. Many electric utilities are considering or already have moved toward implementing asset management concepts and decision-making procedures based on minimizing equipment life-cycle costs and risks. However, the data, analytical tools, and models required for substation equipment risk assessment and management need additional development and enhancement to maximize their value. The tasks in this project provide results to address those needs by developing new metrics and analytics from the collection and analysis of readily available asset performance data (for example, maintenance, condition assessment, failure histories, images, and expert knowledge). For example,

• Offering guidelines on what substation equipment specific data to collect and why?
• Evaluating where advanced analytical techniques (e.g., Artificial Intelligence, Statistical Analysis etc.) can be applied to better understand substation equipment performance. For example, Natural Language Processing to categorize equipment maintenance records into meaningful categories.
• Developing innovative algorithms based on data available from online monitors, digital relays, fault recorders, SCADA, and system operation historians
• Developing new metrics and analytics by collecting and analyzing industry wide performance data for power transformers, circuit breakers, disconnect switches, capacitor banks, substation batteries and protective relays.
• Developing and deliver new versions of asset health algorithms for power transformers and circuit breakers.

Key Activities for 2023

• Provide guidelines on what substation equipment specific data to collect and why?

  • Review and annually update data models (detailed lists of what data should be collected and what types of analysis and decisions such data can support) developed in previous years for transformers, circuit breakers, disconnect switches, instrument transformers, batteries, electromechanical, solid-state, and digital relays, and capacitor coupled voltage transformers.

• Evaluate and apply advanced techniques Improve Substation Equipment Maintenance and Asset Management

  • Both mathematical (e.g., statistical techniques) and artificial intelligence approaches (e.g., Natural language processing (NLP) or machine learning) are researched as part of this task.
  • Utilize data collected from various utilities for transformers, circuit breakers, disconnect switches, , capacitor banks and relays to investigate the applicability of various Natural Language Processing algorithms to analyze free form maintenance and outage records to:
    • organize and curate data
    • categorize maintenance and outage records into meaningful categories and
    • extract actionable information such as dominant issues and trends and non-apparent patterns and relationships.
  • Investigate and develop innovative algorithms based on data available from online monitors, digital relays, fault recorders, SCADA, and system operation historians.

• Power Transformers

  • Population Performance Metrics: Utilize industry wide transformer and tap changer historical failure and in-service population data and advanced statistical analysis techniques to develop hazard rates for various transformer groups. These hazard rates may be used to assess the performance (aging and failure rates) for select groups and to plan capital and spares policies for these groups.
  • Power Transformer Expert System (PTX): Continued development of PTX, a software that utilities can apply to assess individual transformer sub-system conditions (main body, tap changer, bushings, oil quality) and develop equipment risk assessments. Under this effort, continuous progress is made to improve the diagnostic performance of existing algorithms (main body, load tap changer algorithms; rules that account for susceptibility to through-faults). New research is anticipated to better leverage real-time data sources and online monitors to provide more timely assessments of present condition with a minimum of false positives.
  • Advanced Analytics for Power Transformer Condition Assessment is a new task that intends to investigate advanced statistical and machine learning methods for power transformer condition assessment, diagnostics, and monitoring by leveraging EPRI’s expertise and data contributed by utilities to develop the next generation of transformer analytics that might yield quantifiable improvements and advantages over present day analytics.

• Circuit Breakers

  • Population Performance Metrics: Compile and analyze in-service, historical maintenance, replacement, and failure data on different types of circuit breakers from member utilities. define and develop metrics and processes for mining and analyzing these data to develop insights that could lead to better informed decisions regarding maintenance program development; task and timing selection; benchmarking comparison among utilities and breaker makes and models; replacement decision support and specification and selection of new breakers.

  • Replacement and Maintenance Ranking Framework: develops a risk-based tool that will help optimize maintenance, increase reliability, and support capital planning. It uses readily available data and advanced algorithms to characterize a breaker’s relative ranking and need for maintenance or replacement. The ranking enables utilities to scan breaker fleets and identify at-risk units for detailed testing and analysis—a far more cost-effective approach than blanket inspections of an entire fleet.

• Balance of substation assets

  • Substation Battery Performance Assessment compiles and analyzes industry wide substation battery data such as demographic information, inspection, and test results with the goal of developing appropriate analytic approaches to identify maintenance trends, rates, and other factors, better understand the performance characteristics of certain battery populations and how factors, such as age, affect battery performance.
  • Balance of Substation Population Performance Metrics extends the industrywide data collection concept to disconnect switches, relays, capacitor banks, instrument transformers, bushings, and arresters. Researchers are collecting in-service, historical maintenance, replacement, and failure data to develop new metrics and analytics with the goal of enabling utilities to make better informed maintenance, repair/replacement, specification, and selection decisions.

Supplemental Projects and Application Opportunities

• Applying Transmission Asset Analytics Tools and Methodologies
• Spare Strategy Evaluation Methodology Application for Three Phase Power Transformers
• Spare Strategy Evaluation Methodology Development for Mobile Transformers and Banks of Three Single Phase Transformers
• Power Transformer Through Fault Risk Assessment Model Validation and Testing
• Circuit Breaker Replacement and Maintenance Ranking Methodology Application

Engagement Opportunities

For more information, contact: Bhavin Desai, Sr. Program Manager, (704) 804-1188,