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 is condition-based assessment to understand asset health, risk, and action. The end goal is to provide utilities knowledge to make confident, data driven decisions. Applying new analytics to traditional utility datasets may unlock insights into asset health. Exploring new data sources can improve utility situational awareness of their distribution assets. Testing, evaluating, and reporting on new analytical techniques gives utilities confidence to make informed decisions. Key research areas include: collecting industry datasets from traditional sources, exploring new data collection techniques, researching new data analysis techniques, and documenting utility experiences in the form of case studies.
Approach
Research will be provided in multiple forms, including:
- Results from experimentation and analysis
- Reference guides, practical manuals, and training videos
- Workshops, meetings, and webcasts
- Documented reports and case studies
Remote Sensing and Geospatial Analytics for Disaster Recovery
How can utilities leverage next-generation technologies to better plan and respond to destructive weather events? This task explores the use of emerging data collection and analytic techniques to better prepare and respond to major weather events. From satellites to vehicle-mounted cameras, utilities have more options than ever to capture, analyze, and act on remotely sensed data to make data driven decisions. While much of this is feasible, there are few documented case studies. The industry benefits through objective testing and evaluation of these techniques with an overall goal to improve distribution damage prediction, assessment, and reaction.
Distribution Asset Analytics: AMI and Transformer Health Case Studies
How can utilities apply new analytics to traditional data sources to understand asset health, risk, failures, and proactive decision making? This task aims to investigate the use of AMI as a data source to predict or identify failing transformers. In 2025, EPRI plans to evaluate the performance of AMI-based predictions against real-world transformer assets. This work will leverage findings from utility pilots, case studies, and collaboration between EPRI’s P161 and P200 programs.
Applications of AI for Distribution Asset Management
What is the relationship between new data sources and distribution asset health and inventory? Utilities can apply imagery, video, LiDAR, and other remotely sensed data to enable improved distribution asset inspection and inventory. EPRI intends to investigate artificial intelligence and computer vision to automate the analysis of these new data sources. EPRI plans to evaluate these technologies, create analysis ready datasets, test new AI approaches for inspection and inventory, and publish guides for utilities to deploy internally.
Distribution Analytics Utility Case Studies
What are utilities piloting related to distribution analytics; what successes and failures can be shared to industry? EPRI recognizes utilities are growing internal data science capabilities. The intent of this task is to document utility case studies to share with industry. By understanding the state of the art should reduce industry reduplication, and identify common gaps for future research.
Distribution Asset Analytics: Assessing Risk and Data Driven Decisions
How can utilities make confident data driven decisions using new data sources and analysis techniques at distribution scale? Implementing a condition-based maintenance strategy is difficult at such a large scale. There is error in every data source and emerging data analysis techniques have not been studied over a long duration. This task creates a guide and framework for utilities to implement results from EPRI’s research. The intent is to help utilities with the EPRI technology transfer in the rapidly evolving topic of AI, analytics, and asset management.
Research Value
Anticipated benefits to public and funders are:
- Reduced operations and maintenance (O&M) through improved specification of emerging condition monitoring techniques, based on solid facts and repeatable test protocols.
- Third-party, unbiased assessment of distribution asset analytics to aid industry specification and interpretation.
- Improved risk assessment of distribution assets, which translates into improved decision-making.
- Effective knowledge transfer through regular technical webinars, collaborative testing, and documented results in the forms of guides, whitepapers, and technical reports.
- Novel technologies for asset health, monitoring, and tracking—which, in turn, translates into reduced O&M and improved reliability.
- Valuable guidance on the application and effective use of artificial intelligence for distribution asset risk.
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