中国邮电高校学报(英文) ›› 2023, Vol. 30 ›› Issue (3): 41-54.doi: 10.19682/j.cnki.1005-8885.2022.1018

• Artificial Intelligence • 上一篇    下一篇

Transformer fault diagnosis based on relational teacher-student network

Yin Sihan, Li Yalei, Liu Xiaoping, Cui Xu, Wang Huapeng
  

  1. 1. College of Information Science and Engineering, Northeastern University, Shenyang 110819, China 2. Beijing Key Laboratory of Research and System Evaluation of Dispatching Automation Technology, China Electric Power Research Institute, Beijing 100192, China
  • 收稿日期:2021-09-17 修回日期:2022-05-09 出版日期:2023-06-30 发布日期:2023-06-30
  • 通讯作者: 尹思涵 E-mail:xiaohu1596@163.com
  • 基金资助:

    This work was supported by Open Fund of Beijing Key Laboratory of Research and System Evaluation of Dispatching Automation Technology, China Electric Power Research Institute (SGDK 0000DZQT2003377).

Transformer fault diagnosis based on relational teacher-student network

Yin Sihan, Li Yalei, Liu Xiaoping, Cui Xu, Wang Huapeng   

  1. 1. College of Information Science and Engineering, Northeastern University, Shenyang 110819, China 2. Beijing Key Laboratory of Research and System Evaluation of Dispatching Automation Technology, China Electric Power Research Institute, Beijing 100192, China
  • Received:2021-09-17 Revised:2022-05-09 Online:2023-06-30 Published:2023-06-30
  • Contact: Yin Sihan E-mail:xiaohu1596@163.com
  • Supported by:
    This work was supported by Open Fund of Beijing Key Laboratory of Research and System Evaluation of Dispatching Automation Technology, China Electric Power Research Institute (SGDK 0000DZQT2003377).

摘要:

The analysis of dissolved gas in oil can provide an important basis for transformer fault diagnosis. In order to improve the accuracy of transformer fault diagnosis, a method based on the relational teacher-student network (R-TSN) is proposed by analyzing the relationship between the dissolved gas in the oil and the fault type. R-TSN replaces the original hard labels with soft labels, and uses it to measure the similarity between different samples in the space, to a certain extent, it can obtain the hidden feature information in the samples, and clarify the classification boundary. Through the identification experiment, the effect of R-TSN diagnosis model is analyzed, and the influence of the compound fault of discharge and thermal on the diagnosis model is studied. This paper compares R-TSN with support vector machines (SVMs), decision trees and multilayer perceptron models in transformer fault diagnosis. Experimental results show that R-TSN has better performance than the above methods. After adding compound faults in the sample set, the accuracy rate can still reach 86.0%.

关键词:

fault diagnosis, transformer, relational teacher-student network(R-TSN), soft label, gas analysis in oil

Abstract:

The analysis of dissolved gas in oil can provide an important basis for transformer fault diagnosis. In order to improve the accuracy of transformer fault diagnosis, a method based on the relational teacher-student network (R-TSN) is proposed by analyzing the relationship between the dissolved gas in the oil and the fault type. R-TSN replaces the original hard labels with soft labels, and uses it to measure the similarity between different samples in the space, to a certain extent, it can obtain the hidden feature information in the samples, and clarify the classification boundary. Through the identification experiment, the effect of R-TSN diagnosis model is analyzed, and the influence of the compound fault of discharge and thermal on the diagnosis model is studied. This paper compares R-TSN with support vector machines (SVMs), decision trees and multilayer perceptron models in transformer fault diagnosis. Experimental results show that R-TSN has better performance than the above methods. After adding compound faults in the sample set, the accuracy rate can still reach 86.0%.

Key words: fault diagnosis, transformer, relational teacher-student network(R-TSN), soft label, gas analysis in oil

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