Overview
Project Description
Key Research Questions
Distribution systems are composed of many assets that are distributed over a wide geographic area. Many of these assets are near or past their expected service life. Typically, an individual asset’s low cost makes online monitoring or testing difficult to justify, but the cumulative impact of aging equipment can have significant reliability and cost implications. Distribution asset managers are thus faced with the unique challenge of addressing aging infrastructure—and the associated risk—with minimal tools and information to support decision making.
Many electric utilities are considering or have implemented asset management programs to minimize equipment life-cycle costs and risks, with much of the effort historically targeted at the more expensive transmission components, such as substation power transformers. These approaches could provide significant value to distribution systems. However, the data, analytical tools, and models required for distribution assets are not well established. At the same time, utilities are increasingly challenged with being able quantify, justify, and measure the effectiveness of investments in assets to bolster reliability and resiliency.
Objective
The objective of this project is to advance the state of condition-based assessment for electric distribution assets to better understand asset health, quantify risk, and support data-driven decision making. This research aims to help utilities transform traditional and emerging data sources into actionable insights that improve reliability, reduce costs, and inform asset strategies. The end goal is to provide utilities with scalable tools, trusted evaluations, and collaborative forums that promote effective adoption of analytics, artificial intelligence (AI), and asset life modeling.
Approach
The following tasks will be undertaken to address key questions related to adopting and applying artificial intelligence and statistical analysis techniques to enable data-driven asset management in the electric distribution system:
- AI-Enabled Asset Health and Asset Management Systems Interest Group: A forum for exchanging information, experiences, and lessons learned; identifying knowledge gaps, needed capabilities, and research topics; and exploring new AI-enabled tools and methodologies for enhancing asset health indices, analytics, and overall asset management. AI topics covered may include predictive maintenance systems, anomaly detection, health monitoring, image analysis, large language models, and optimization algorithms.
- Asset Management and Applied Analytics Workshops: EPRI plans to conduct an in-person workshop to facilitate information exchange and promote industry-wide collaboration and discussion on important concepts and techniques for asset management. The workshops may include:
- Discussions of current and emerging industry issues where analytics can help
- Utility presentations sharing ongoing (or planned) efforts
- Distribution Analytics Utility Use Cases: This task documents case studies from participating utilities that showcase the application of analytics to assess distribution asset performance, health, and risk assessment. These case studies will focus on what worked, what didn’t, and the context for success or limitations, helping to reduce duplication across the industry. Definitions and Data Models for Distribution Asset Analysis: This task develops and updates data models for efficient and effective extraction, transfer, and loading of inspection, performance, and failure data for use in industry and utility database applications and performance analytics. Data models for pad-mount, underground, and network transformers, underground cables, and wood poles will be reviewed. This task plans to work with utilities to develop a comprehensive prioritized list of additional distribution assets for which data models may be developed in future years.
- Collection and Analysis of Industry-wide Distribution Asset Performance and Failure Data: This task compiles and analyzes historical failure and performance data on distribution assets in a common format, using information gathered from participating utilities. This research defines and develops metrics and processes for mining and analyzing these datasets. In 2026, EPRI plans to continue developing insights intended to better inform decisions regarding maintenance program development, task and timing selection, benchmarking comparison among utilities, replacement decision support, and specification and selection of new distribution system assets.
- Analytics for Fleet Management Distribution Assets: This task investigates and develops performance assessment analytics for distribution assets, such as wood poles;, underground cables; and pad-mount, underground, and network distribution transformers. The analytics are developed using data mining and analysis of periodic inspection results, subject matter expert experience, and other inputs, such as asset family, make, model, manufacturer, and operating environment. The research focuses on enhancements to algorithms and analytic methods for assets such as wood poles, underground cables, and distribution transformers.
- Adopting Artificial Intelligence: This task explores opportunities to apply, scale, and adopt artificial intelligence in distribution asset management. Topics may include large language models for data analysis and interpretation, computer vision for asset inspection, and other novel methods. This task focuses on evaluating feasibility, identifying constraints, and producing guidance for implementation.
- Technology Scouting and Innovation Monitoring: The pace of innovation in distribution asset analytics is accelerating, with new tools, methodologies, vendors, and service providers emerging rapidly. This task aims to proactively identify, monitor, and assess new technologies relevant to utility data analytics. In addition to core utility offerings, this effort will track advancements in adjacent sectors—such as transportation, telecommunications, and smart cities—to surface transferable innovations. Insights from this scouting will inform future research, pilot opportunities, and collaborative demonstrations.
Research Value
This research aims to create value for participating organizations and the public by:
- Enabling utilities to make confident, data-informed decisions that reduce risk and improve reliability
- Facilitating benchmarking and knowledge sharing through facilitated collaboration between utilities and subject matter experts
- Leading specification and accelerating the operational deployment of emerging AI technologies for real-world utility needs
- Documenting utility-vetted case studies that document successful practices, failures, and lessons learned
- Developing guidance for utilities to apply asset life-cycle modeling, improving capital planning and maintenance prioritization
- Advancing industry understanding of how environmental, material, and operational variables affect asset life and health
Asset and Analytics Task Force
The Asset and Analytics Task Force advises the Asset and Analytics Project (P180.005). This task force meets several times per year by WebEx or in person. There is usually one in-person meeting per year held in conjunction with the other P180 task forces.
Members are encouraged to participate in several ways:
- Help identify gaps in current research
- Make us aware of utility challenges and provide input to analytical approach
- Contribute data
- Share utility experiences and research application successes at task force and advisory meetings
- Attend task-force meetings
This task force is also a good opportunity to meet automation experts at other participating companies.
Common Questions
Who can attend task-force meetings?
- Task-force meetings are for funders of Program 180 or P180.005 project. This includes task-force members and guests from sponsoring companies.
How do I join this task force?
- Just send a request to Dexter Lewis or Kim Thach. Similarly, if you’d like to be removed, let one of them know.
Can my company have more than one task-force member?
- Yes.
Can I share task-force material within my company?
- Yes.
Are discussions covered by a non-disclosure agreement?
- Yes. All EPRI member agreements include non-disclosure clauses.
If my company isn’t funding this, how can I sign up?
- Each company has their own methods for selecting components of the annual EPRI research portfolio. Contact your METT for more information. Technical advisors from EPRI’s member services can also help. Find contact information here.
Related Research
Collaborative Supplemental Projects
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Improving Grid Safety and Resilience During Extreme Weather Events and Wildfires
This project intends to improve resiliency and reduce wildfire risks by providing an objective technical basis for advanced distribution system design, protection, and management techniques.
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For more on supplemental projects, click here. To discuss project ideas, please e-mail Doug Dorr.
Other Programs
P200 Distribution Operations and Planning
- PS200C: Operations
- PS200D: Protection
P34 Distribution Operations and Planning
- P34.001: Transmission Asset Management Analytics: Principles and Practices
- P34.003: Overhead Transmission Asset Analytics
- P34.004: Underground Transmission Asset Analytics