辽宁石油化工大学学报

辽宁石油化工大学学报 ›› 2020, Vol. 40 ›› Issue (5): 79-85.DOI: 10.3969/j.issn.1672-6952.2020.05.014

• 信息与控制工程 • 上一篇    下一篇

面向电力变压器油中溶解气体的卷积神经网络诊断方法

裴小邓罗林陈帅王乔   

  1. 辽宁石油化工大学 信息与控制工程学院,辽宁 抚顺 113001
  • 收稿日期:2020-05-05 修回日期:2020-05-21 出版日期:2020-10-30 发布日期:2020-11-12
  • 通讯作者: 罗林(1984⁃),男,博士,讲师,从事输变电装备在线监测与智能故障诊断的研究;E⁃mail:luolin@lnpu.edu.cn
  • 作者简介:裴小邓(1996-),男,硕士研究生,从事变压器智能故障诊断方向研究;E-mail:peixiaodeng@163.com
  • 基金资助:
    国家自然科学基金项目(61703191); 辽宁省教育厅青年基金项目(L2017LQN028); 辽宁石油化工大学科研启动基金项目(2017XJJ?012)

A Convolutional Neural Network Diagnosis Method for Dissolved Gas in Power Transformer Oil

Pei XiaodengLuo LinChen ShuaiWang Qiao   

  1. School of Information and Control Engineering,Liaoning Shihua University,Fushun Liaoning 113001,China
  • Received:2020-05-05 Revised:2020-05-21 Published:2020-10-30 Online:2020-11-12

摘要: 油中溶解气体分析法(Dissolved Gas Analysis,DGA)是判断变压器内部故障的重要方法之一。针对传统基于浅层的机器学习方法在变压器故障诊断中存在的特征提取和泛化能力方面的不足,提出了一种基于卷积神经网络的变压器故障诊断方法。利用网络中的卷积层对油中溶解气体进行特征转换,结合池化层强化重要特征的能力,对故障敏感特征进行提取。通过实验研究了卷积核数目、卷积核大小、池化层、网络深度对模型诊断性能的影响。通过混淆矩阵、ROC曲线和PR曲线对比分析了卷积神经网络模型、支持向量机(Support Vector Machine,SVM) 模型、BP神经网络(Back Propagation Neural Network,BPNN)模型。实验结果表明,卷积神经网络模型的诊断性能更为优秀。

关键词: 油中溶解气体, 变压器, 故障诊断, 卷积神经网络

Abstract: Dissolved Gas Analysis (DGA) is one of the important methods for determining transformer internal faults.Aiming at the shortcomings of the traditional shallow⁃based machine learning method in transformer fault diagnosis in feature extraction and generalization ability, a transformer fault diagnosis method based on Convolutional Neural Network (CNN) was proposed. The convolution layer in the network was used for feature conversion of dissolved gas in oil, and fault sensitive features were extracted by combining with the ability of pooling layer to strengthen important features. The effects of the number of convolution kernels, the size of convolution kernels, pooling layer and network depth on the diagnostic performance of the model were studied experimentally. The models of Convolutional Neural Network, Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN) were compared and analyzed by confounding matrix, ROC curve and PR curve. The experimental results show that the Convolutional Neural Network model has better diagnostic performance.

Key words: Dissolved gas in oil, Transformer, Fault diagnosis, Convolutional neural network

引用本文

裴小邓, 罗林, 陈帅, 王乔. 面向电力变压器油中溶解气体的卷积神经网络诊断方法[J]. 辽宁石油化工大学学报, 2020, 40(5): 79-85.

Pei Xiaodeng, Luo Lin, Chen Shuai, Wang Qiao. A Convolutional Neural Network Diagnosis Method for Dissolved Gas in Power Transformer Oil[J]. Journal of Liaoning Petrochemical University, 2020, 40(5): 79-85.

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链接本文: http://journal.lnpu.edu.cn/CN/10.3969/j.issn.1672-6952.2020.05.014

               http://journal.lnpu.edu.cn/CN/Y2020/V40/I5/79