A Fault Identification Method for Metal Oxide Arresters Combining Suppression of Environmental Temperature and Humidity Interference with a Stacked Autoencoder

Shengwen Shu (), Xiaoyao Zhang, Guobin Wang, Jinglan Zeng and Ying Ruan
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Shengwen Shu: School of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
Xiaoyao Zhang: School of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
Guobin Wang: Electric Power Research Institute, State Grid Fujian Electric Power Co., Ltd., Fuzhou 350007, China
Jinglan Zeng: Electric Power Research Institute, State Grid Fujian Electric Power Co., Ltd., Fuzhou 350007, China
Ying Ruan: Electric Power Research Institute, State Grid Fujian Electric Power Co., Ltd., Fuzhou 350007, China

Energies, 2023, vol. 16, issue 24, 1-18

Abstract: Most existing methods aiming to solve the fault identification problem of metal oxide arresters (MOAs) are limited by strong subjectivity in judgment, the significant impact of environmental temperature and humidity on the online monitoring of the resistance current, and poor generalization ability. Therefore, in this article, we propose an MOA fault identification method that combines suppressing environmental temperature and humidity interference with a stacked autoencoder (SAE). Firstly, a functional relationship model between resistance current and environmental temperature and humidity is established. Then, a temperature and humidity interference suppression method based on weighted nonlinear surface modeling is proposed to normalize the resistance current to the same reference temperature and humidity conditions. Finally, an MOA fault identification method combining the suppression of environmental temperature and humidity interference with an SAE is proposed. Furthermore, a comprehensive comparison is conducted on the recall, accuracy, F 1 -score, and average accuracy of support vector machine, random forest, logistic regression, and SAE classification algorithms in three different scenarios to demonstrate the effectiveness of the proposed method. The results indicate that environmental temperature and humidity interference suppression for resistive current prior to MOA fault classification significantly reduce the number of false alarms. Compared with other methods, the MOA fault identification method, which combines environmental temperature and humidity interference suppression with an SAE, has the highest average accuracy of 99.7%.

Keywords: metal oxide arrester (MOA); fault identification; environmental temperature and humidity; interference suppression; stacked autoencoder (SAE) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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