Power transformer fault diagnosis considering data imbalance and data set fusion

Zhang, Yang1; Chen, Hong Cai2,3; Du, Yaping1; Chen, Min4; Liang, Jie5; Li, Jianhong5; Fan, Xiqing6; Yao, Xin7

2020-12-01

10.1049/hve2.12059

High Voltage   影响因子和分区

2397-7264

Improving the accuracy of transformer dissolved gas analysis is always an important demand for power companies. However, the requirement for large numbers of fault samples becomes an obstacle to this demand. This article creatively uses a large number of health data, which is much easier to obtain by power companies, to improve diagnosis accuracy. Comprehensive investigations from the view of both data set and methodology to deal with this problem are presented. A data set consists of 9595 health samples and 993 fault samples is used for analysis. The characteristics of the data set and the influence of the health data on diagnostic accuracy are discussed. The performance of many state-of-art algorithms that handle the imbalanced problem is evaluated. Meanwhile, an efficient fault diagnosis algorithm named self-paced ensemble (SPE) is presented. In SPE, classification hardness is proposed to include the data characteristic in the classification. This method can guarantee the diversity of the data set and keep high performance. According to the experiment results, the superior of SPE is confirmed and also proves that involving more health samples can improve transformer diagnosis when fault data are limited.

SCI ; EI

英语

通讯

Science and Technology Project of State Grid Corporation of China[5500202019090A-0-0-00]

Engineering

Engineering, Electrical & Electronic

WOS:000607289000001

WILEY

20212510531184

Electric utilities ; Failure analysis ; Health ; Power transformers

Medicine and Pharmacology:461.6 ; Electric Power Lines and Equipment:706.2

Web of Science

1.Hong Kong Polytech Univ, Dept Bldg Serv Engn, Hong Kong, Peoples R China
2.Southern Univ Sci & Technol, Acad Adv Interdisciplinary Studies, Shenzhen 518057, Peoples R China
3.Southern Univ Sci & Technol, Dept Elect & Elect Engn, Shenzhen 518057, Peoples R China
4.State Grid Zhejiang Elect Power Co Ltd, Res Inst, Hangzhou, Peoples R China
5.Zhejiang Huayun Informat Technol Co Ltd, Hangzhou, Peoples R China
6.State Grid Zhejiang Elect Power Co Ltd, Lishui Power Supply Bur, Lishui, Peoples R China
7.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Guangdong Prov Key Lab Brain Inspired Intelligent, Shenzhen, Peoples R China

Zhang, Yang,Chen, Hong Cai,Du, Yaping,et al. Power transformer fault diagnosis considering data imbalance and data set fusion[J]. High Voltage,2020,6:543-554.

Zhang, Yang.,Chen, Hong Cai.,Du, Yaping.,Chen, Min.,Liang, Jie.,...&Yao, Xin.(2020).Power transformer fault diagnosis considering data imbalance and data set fusion.High Voltage,6,543-554.

Zhang, Yang,et al."Power transformer fault diagnosis considering data imbalance and data set fusion".High Voltage 6(2020):543-554.