A restoration-clustering-decomposition learning framework for aging-related failure rate estimation of distribution transformers

https://doi.org/10.1016/j.ress.2022.109043Get rights and content
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Highlights

  • The developed learning framework only relies on the available non-intrusive data

  • A long-existing problem, i.e., aging failure data restoration, is raised and solved

  • The degree of polymerization is selected as the aging covariate

  • The practicality of the framework is verified by a practical application

Abstract

Distribution transformers (DTs) are critical components used in power distribution networks, and they are vulnerable to aging failures due to irreversible insulation degradation. Therefore, the accurate estimation of the aging-related failure rates (AFRs) is necessary for the reliability-centered maintenance and replacement strategies needed for ensuring service reliability and safety. Various data-intensive models have been proposed for AFR evaluation of power equipment. However, these models cannot be used for AFR evaluation of DTs due to the limitation of the available data. This paper tackles this important problem in an unconventional way by it develops a novel Restoration-Clustering-Decomposition learning framework to model the AFRs of individual DTs and improve evaluation accuracy. The proposed approach requires only the non-intrusive data that can be directly extracted from existing available databases, making it feasible to be applying to numerous DTs. First, the analysis of the degree of polymerization (DP) degradation and the Latin Hypercube sampling (LHS) technique are combined to reproduce aging failure data. Then, an optimal Entropy-weighted K-means (EW-K-means) clustering method and the classic 2-parameter Weibull model are used to evaluate the average AFRs of different DT groups through failure data analysis. Then, a DP-based decomposition function is introduced to quantify the relative aging degree of in-group individuals and to derive the probabilistic AFRs of each DT in the group. Application examples of a scrapped DT population in Chongqing Electric Power Company of China are presented and discussed in detail. The results show that the proposed learning framework has a promising capability for AFR evaluation of individual DTs and bears great practicality in the real world.

Keywords

Power equipment reliability
Distribution transformers (DTs)
Aging-related failure rate (AFR)
Data restoration
Transformer clustering, Decomposition function

Data Availability

  • Some data used during the study were provided by a third party.

Cited by (0)

Wei Huang received the B.S. degree from China University of Petroleum, Qingdao, in 2019. He is pursuing his Ph. D degree in the School of Electrical Engineering of Chongqing University, China. His research interests include power system reliability and asset management.

Changzheng Shao received the B.S. degree in electrical engineering from Shandong University and the Ph. D degree in electrical engineering from Zhejiang University in 2015 and 2020, respectively. He is currently an assistant professor at Chongqing University, Chongqing, China. His research interests include the operation optimization and reliability evaluation of the integrated energy system.

Bo Hu received the Ph.D. degree in electrical engineering from Chongqing University, Chongqing, China, in 2010. He is a Professor with the School of Electrical Engineering, Chongqing University. His main research interests focus on areas of power system reliability, planning and analysis.

Weizhan Li received the B.S degree in electrical engineering from Guizhou University, Guiyang, China, in 2020. He is currently working toward the M.S degree with Chongqing University, Chongqing, China. His research interests include power system operation optimization and reliability.

Kaigui Xie received the Ph.D. degree in power system and its automation at Chongqing University in 2001. Currently, he is a full professor in the School of Electrical Engineering, Chongqing University. His main research interests focus on the areas of power system reliability, planning and analysis. He is the author and co-author of over 200 academic papers and five books.

Enrico Zio received the B.Sc. and Ph.D. degrees in nuclear engineering from the Politecnico di Milano, Milan, Italy, in 1991 and 1995, respectively, the M.Sc. degree in mechanical engineering from the University of California at Los Angeles (UCLA), Los Angeles, CA, USA, in 1995, and the Ph.D. degree in nuclear engineering from the Massachusetts Institute of Technology (MIT), Cambridge, MA, USA, in 1998. His research interests include modeling of the failure–repair–maintenance behavior of components, complex systems for the analysis of their reliability. He is the chairman and co-chairman of several international Conferences, associate editor of several international journals, and a Referee of more than 20. Prof. Zio was an editor of the Reliability Engineering & System Safety.

Wenyuan Li is currently a professor with Chongqing University, Chongqing, China. He used to work at BC Hydro as a Principal Engineer and won multiple technical awards. His research interests include power system asset management, planning, operation, optimization, and reliability assessment. He is a Fellow of the Canadian Academy of Engineering and the Engineering Institute of Canada, and a Foreign Member of the Chinese Academy of Engineering.