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《Electrical Measurement & Instrumentation》 2017-13
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Fault diagnosis method of transformer based on convolutional neural network

Jia Jinglong;Yu Tao;Wu Zijie;Cheng Xiaohua;School of Electric Power,South China University of Technology;  
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.
【Fund】: 国家重点基础研究发展计划(973计划)(2013CB228205);; 国家自然科学基金资助项目(51477055)
【CateGory Index】: TM407;TP183
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【Citations】
Chinese Journal Full-text Database 10 Hits
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【Co-citations】
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【Secondary Citations】
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