Research & Technical Content

Key takeaways, results from ongoing research tasks and how they are applied in informing asset management decisions.

Substation Equipment Spares Strategy Evaluation Model Development

There is a growing concern among utilities due to increasing transmission asset delivery lead times. To help address the concern, EPRI researchers are developing a methodology to help utilities understand the risks associated with different strategies for ordering and stocking substation equipment spares.

Circuit Breaker Analytics

  • Establish the value and use of various types of circuit breaker performance data (e.g., work orders, defects, failure records, relay data etc.)

  • Collect and analyze industrywide data to develop maintenance, asset management and model specific insights

  • Utilize the insights and utility’s fleet data to develop maintenance and replacement ranking framework

  • Guide utilities on what data they should have access to for asset management analytics

Balance of Substation (all substation assets other than power transformers and circuit breakers) Analytics aims to extend the industry-wide data (IDB) collection concept beyond transformers, batteries, relays and breakers to other equipment, such as disconnect switches, capacitor banks, instrument transformers, bushings, and arresters. The goal is to utilize the collected data to develop analytics to better understand balance of substation asset performance, failure and replacement rates, and maintenance trends. Ongoing tasks:

  • Continue to populate the balance of substation IDB by collecting in-service, historical maintenance, replacement, and failure data.

  • Develop new metrics and analytics with the goal of enabling utilities to make better-informed maintenance, repair/replacement, specification, and selection decisions.

  • Investigate the utilization of performance metrics in the development of fleet management approaches.

  • Issue reports summarizing the developments and applications for decision support for asset managers, insights on equipment performance, and utility use cases.

Advanced Analytics (e.g., Machine Learning, Natural Language Processing etc.) evaluates various techniques to identify whether they can be used:

  • To develop methods to transform a variety of asset performance data into an analysis ready format

  • To develop methods to analyze the transformed data

  • To categorize maintenance and outage records into meaningful categories

  • To extract actionable information such as dominant issues and trends, non-apparent patterns and relationships

  • For predictive analytics.

Transformer Asset Health Visualization– provides EPRI’s perspective on transformer asset health visualization for different utility stakeholders including asset managers, subject matter experts and maintenance personnel. The following topics are addressed:

  • Input data requirements and priority of data fields

  • Visualization of transformer health information for different stakeholders

    • Fleet Management View
    • Asset Health View
    • Operational Status View

  • Priority of alarms