电测与仪表2017，Vol.54Issue(13)：62-67,6.
基于卷积神经网络的变压器故障诊断方法
Fault diagnosis method of transformer based on convolutional neural network
摘要
Abstract
Transformer is an important equipment in power system, its security and stability directly affect the healthy development of the national economy.Dissolved gas analysis (DGA) is a key method of transformer fault analysis.The convolutional neural network, as an important model of deep learning, has strong classification ability, which is widely used in image recognition, speech processing, and so on.The content of five kinds of dissolved gases is selected as the input of the model in this paper.On the basic of analysis method of dissolved gases by using BP neural network, according to the shortcomings that BP neural network is insufficient in expression ability and easy to over-fitting, the application of convolutional neural network is proposed to diagnose transformer fault in this paper.Moreover, its simulation proves that the proposed method has a better performance compared with BP neural network.Additionally, the effect of convolution kernel number, kernel size and sampling width of convolutional neural network on the classification results is discussed in this paper.关键词
变压器/油中溶解气体分析/故障诊断/卷积神经网络Key words
transformer/dissolved gas analysis/fault diagnosis/convolutional neural network分类
信息技术与安全科学引用本文复制引用
贾京龙,余涛,吴子杰,程小华..基于卷积神经网络的变压器故障诊断方法[J].电测与仪表,2017,54(13):62-67,6.基金项目
国家重点基础研究发展计划(973计划)(2013CB228205) （973计划）
国家自然科学基金资助项目(51477055) （51477055）
