Identification Method of Transformer Fault Based on Data Feature Enhancement and Residual Shrinkage Network

被引：0
|
作者：
Ma X. [1 ]
Shang Y. [2 ]
Hu H. [1 ]
Xu Y. [3 ]
机构：
[1] School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou
[2] China Institute of Water Resources and Hydropower Research, Beijing
[3] China Yangtze Power Co., Ltd., Yichang
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2022年 / 46卷 / 03期
关键词：
Deep residual shrinkage network; Fault identification; Feature gas; Transformer;
D O I：
10.7500/AEPS20210308004
中图分类号：
学科分类号：
摘要：
In order to enhance the ability of the deep residual shrinkage network to learn the features of transformer faults and improve the accuracy of fault identification, the fault feature gas vector with an improved deep residual shrinkage network is constructed to identify transformer faults. First, a variable soft threshold function is constructed to eliminate the effects of constant deviations, and the fast back tracking algorithm is used to speed up the threshold determination and ensure the integrity of the output results. Then, a cross-entropy function with variable weights is proposed to reduce the effects of misrecognition on the network accuracy. The constructed feature gas vector is used as the input to ensure that the network learns and recognizes the features of more fault factors. Finally, taking overheating faults and arcing faults as samples, the experimental results verify the effectiveness of the method. Compared with the traditional method, the recognition accuracy of the proposed method is higher, and it is suitable for the identification of multi-feature faults in the power system. © 2022 Automation of Electric Power Systems Press.
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页码：175 / 183
页数：8
相关论文
共 33 条
  • [1] BENHMED K, MOOMAN A, YOUNES A, Et al., Feature selection for effective health index diagnoses of power transformers, IEEE Transactions on Power Delivery, 33, 6, pp. 3223-3226, (2018)
  • [2] WANG Ke, LI Jinzhong, ZHANG Shuqi, Et al., New features derived from dissolved gas analysis for fault diagnosis of power transformers, Proceedings of the CSEE, 36, 23, pp. 6570-6578, (2016)
  • [3] GHONEIM S S M., Intelligent prediction of transformer faults and severities based on dissolved gas analysis integrated with thermodynamics theory, IET Science, Measurement & Technology, 12, 3, pp. 388-394, (2018)
  • [4] DUVAL M., A review of faults detectable by gas-in-oil analysis in transformers, IEEE Electrical Insulation Magazine, 18, 3, pp. 8-17, (2002)
  • [5] MANSOUR D E A., Development of a new graphical technique for dissolved gas analysis in power transformers based on the five combustible gases, IEEE Transactions on Dielectrics and Electrical Insulation, 22, 5, pp. 2507-2512, (2015)
  • [6] FAIZ J, SOLEIMANI M., Dissolved gas analysis evaluation in electric power transformers using conventional methods: a review, IEEE Transactions on Dielectrics and Electrical Insulation, 24, 2, pp. 1239-1248, (2017)
  • [7] BAKAR N A, ABU-SIADA A, ISLAM S., A review of dissolved gas analysis measurement and interpretation techniques, IEEE Electrical Insulation Magazine, 30, 3, pp. 39-49, (2014)
  • [8] KARI Tusongjiang, GAO Wensheng, ZHANG Ziwei, Et al., Power transformer fault diagnosis based on a support vector machine and a genetic algorithm, Journal of Tsinghua University (Science and Technology), 58, 7, pp. 623-629, (2018)
  • [9] LI Jiangeng, LI Lijie, ZHANG Yan, Et al., Method for training convolution neural network with multiple classifiers, Journal of Beijing University of Technology, 44, 10, pp. 1291-1296, (2018)
  • [10] SHI Huijie, GE Fei, DING Ming, Et al., Research on on-line assessment of transmission network operation risk, Power System Technology, 29, 6, pp. 43-48, (2005)
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