Experts are regularly asked by the asset and finance group to provide a list of transformers most likely to fail or in the poorest condition for a proactive replacement project. The response is regionally based, with various spreadsheets, different analyses and different collations, as some experts have more than 1000 transformers to evaluate. Then a call comes in about a failed transformer that is not on any of the supplied lists. Such failures are inevitable: Not every failure is driven by condition-related failure modes, and not every failure is predictable.
The first step in the development of a useful health and risk management (HRM) tool was to invest in data cleanup and subsequent data-hygiene management. This is an ongoing task and requires constant vigilance to prevent rogue data errors from causing false positives in analyses. Data is made available through a single-user interface, and standard engineering algorithms are applied to identify issues and data needing deeper analysis. Condition-based maintenance (CBM), load variation, oil test, electrical test and work order data all provide context in one interface for decision support.
Analytics such as the Doble Frank scores, TOA4 gassing scores and severity, and Electric Power Research Institute (EPRI) PTX indices are applied initially, and the results are normalized as a linear feature set that can be analyzed with a supervised ML tool. The combination of approaches allows data related to each transformer to be classified into one of several predefined classifications or states: normal, monitor, service, stable, replace and risk identified.
The SciML tool takes the best of both worlds, applies standards and guidelines, and benefits from the broad application of ML. The process at Duke Energy has reduced time for experts to perform annual fleet evaluations to a few days, rather than several weeks, in a consistent manner across the organization. The number of bad actors slipping through the cracks is lower, but not yet zero.
One of the features of the hybrid system is the ability of the system to change some states automatically:
- A state may be changed automatically to “monitor” or “service” based on raw data.
- The state may be changed to “risk identified” based on engineering analytics and ML classification.
- No transformer state can be automatically changed to “stable” or “replace,” as those states require expert intervention. After reviewing the data, the expert determines whether a transformer is stable or should be marked for replacement, with comments recorded.
Duke Energy’s hybrid model of engineered analytics and ML has proven to be an excellent but imperfect tool — far more accurate than either pure AI/ML tools or engineered analytics alone. The transformer state updated by experts is now far more useful in making sound planning decisions.
Success, in terms of uptake and use of the hybrid model, has been based on numerous activities: data hygiene, collation of data sources, application of standards and guidelines for engineered analytics, data normalization for features to feed the ML, continuous expert input and refinement in a closed-loop evaluation.
The hybrid approach has enabled experts and field technicians to focus on important and critical cases. The system is not perfect, but it has identified bad actors more consistently and more accurately than any previous approach used at Duke Energy.
Experts Are The Key
AI/ML tools can provide benefits in interpreting and classifying complex data, but they can be fooled by data that is inconsistent with their training set. ML tools require input from experts who can guide tool development in specific applications.
Understanding the raw data and making the best use of data-hygiene and data-management activities is a base for building an overall analysis system that combines best practices, application of standards and guidelines, and targeted use of AI/ML systems. Doble Engineering has shown developing targeted AI/ML tools can bring benefits to practical data analysis in the field and applying targeted ML tools can support experts in their asset performance analyses.
Acknowledgments
The authors would like to thank their colleagues at Duke Energy, Doble Engineering Co. and many more across the industry who have provided comments, feedback and discussion of the application of AI techniques. Many thanks to Dr. Mitiche at Glasgow Caledonian University for sharing her results of AI analysis of PD/EMI data.
This article was provided by the InterNational Electrical Testing Association. NETA was formed in 1972 to establish uniform testing procedures for electrical equipment and systems. Today the association accredits electrical testing companies; certifies electrical testing technicians; publishes the ANSI/NETA Standards for Acceptance Testing, Maintenance Testing, Commissioning, and the Certification of Electrical Test Technicians; and provides training through its annual PowerTest Conference and library of educational resources.
This article is published in tribute to Tom Rhodes who sadly passed away recently.
Dr. Tony McGrail of Doble Engineering Company provides condition, criticality, and risk analysis for substation owner/operators. Previously, he spent over 10 years with National Grid in the UK and the US as a Substation Equipment Specialist, with a focus on power transformers, circuit breakers, and integrated condition monitoring. Tony also took on the role of Substation Asset Manager to identify risks and opportunities for investment in an ageing infrastructure. He is an IET Fellow, past-Chairman of the IET Council, a member of IEEE, ASTM, ISO, CIGRE, and IAM, and a contributor to SFRA and other standards.
Tom Rhodes graduated from the Upper Iowa University with a BS in professional chemistry. He had over 30 years of data analysis for asset management of industrial systems. Rhodes worked as Implementer/Project Leader at CHAMPS Software implementing new CMMS/asset management technology, and held titles of Sr. Science and Lab Services Specialist, Scientist, and Lead Engineering Technologist at Duke Energy.
