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

Xin MA; Yizi SHANG; Hao HU; Yang XU

doi:10.7500/AEPS20210308004

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

Application Research | 更新时间：2025-06-16

- Identification Method of Transformer Fault Based on Data Feature Enhancement and Residual Shrinkage Network
- Automation of Electric Power Systems Vol. 46, Issue 3, Pages: 175-183(2022)
- 作者机构：
  
  1.华北水利水电大学电力学院,河南省郑州市 450045
  2.中国水利水电科学研究院,北京市 100038
  3.中国长江电力股份有限公司,湖北省宜昌市 443002
- 作者简介：
- 基金信息：
  
  National Key R&D Program of China(2019YFC0409000)
- DOI：10.7500/AEPS20210308004
  CLC：
- Received：08 March 2021，
  
  Revised：2021-05-25，
  
  Published Online：13 September 2021，
  
  Published：10 February 2022
- 稿件说明：
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马鑫,尚毅梓,胡昊等.基于数据特征增强和残差收缩网络的变压器故障识别方法[J].电力系统自动化,2022,46(3):175-183.

MA Xin,SHANG Yizi,HU Hao,et al.Identification Method of Transformer Fault Based on Data Feature Enhancement and Residual Shrinkage Network[J].Automation of Electric Power Systems,2022,46(3):175-183.
马鑫,尚毅梓,胡昊等.基于数据特征增强和残差收缩网络的变压器故障识别方法[J].电力系统自动化,2022,46(3):175-183. DOI： 10.7500/AEPS20210308004.

MA Xin,SHANG Yizi,HU Hao,et al.Identification Method of Transformer Fault Based on Data Feature Enhancement and Residual Shrinkage Network[J].Automation of Electric Power Systems,2022,46(3):175-183. DOI： 10.7500/AEPS20210308004.

摘要

为增强深度残差收缩网络对变压器故障特征的学习能力从而提高故障识别精度，文中研究构建了故障特征气体向量配合改进的深度残差收缩网络来识别变压器故障。首先，构建可变软阈值函数消除恒定偏差的影响，利用快速回溯算法加快阈值确定速度的同时确保输出结果的完整性。然后，提出带可变权重的交叉熵函数降低误识别对网络精度的影响，并将构建的特征气体向量作为网络输入，保证网络学习并识别更多故障因素的特征。最后，以过热故障和电弧放电故障为样本的实验结果验证了该方法的有效性。与传统方法相比，所提方法的识别精度高，而且适用于电力系统多特征故障识别。

Abstract

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.

关键词

Keywords

references

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