Residential transformer population is a critical type of asset that many electric utility companies have been attempting to manage proactively and effectively to reduce unexpected transformer failures and life loss that are often caused by overloading. Within the typical power asset portfolio, the residential transformer asset is often large in population, has the lowest reliability design, lacks transformer loading data and is susceptible to customer loading behaviors, such as adoption of distributed energy resources and electric vehicles. On the bright side, the availability of more residential service operation data along with the advancement of data analytics techniques has provided a new path to further our understanding of residential transformer overloading risk statistically. This research developed a new data-driven method that combines a transformer temperature rise and insulation life loss simulation model with clustering analysis technique. It quantitatively and statistically assesses the overloading risk of residential transformer population in one area and suggests proper risk management measures according to the assessment results. Multiple application examples for a Canadian utility company have been presented and discussed in detail to demonstrate the applicability and usefulness of the proposed method. Index Terms-power system reliability, clustering methods, transformers, life estimation, unsupervised learning. Ming Dong (S ' 08 , M ' 13, SM'18) received his doctoral degree from in 2013. Since graduation, he has been working in various roles in two major electric utility companies in West Canada as a Professional Engineer (P.Eng.) and Senior Engineer for more than 5 years. In 2017, he received the Certificate of Data Science and Big Data Analytics from Massachusetts Institute of Technology. He is also a regional officer of Alberta Artificial Intelligence Association. His research interests include applications of artificial intelligence and big data technologies in power system planning and operation, power quality data analytics, power equipment testing and system grounding. Alexandre Nassif (S'05, M'09, SM'13) is a specialist engineer in ATCO Electric. He published more than 50 technical papers in international journals and conferences in the areas of power quality, DER, microgrids and power system protection and stability. Before joining ATCO, he simultaneously worked for Hydro One as a protection planning engineer and Ryerson University as a post-doctoral research fellow. He holds a doctoral degree from the University of Alberta and is a Professional Engineer in Alberta. Benzhe Li (M'18) received his Master's degree from Department of Electrical and Computer Engineering, University of Alberta, Canada in 2015. He is currently an electrical engineer with Energy Ottawa. His research interests include advanced power quality data analytics and equipment condition monitoring.
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