doi:10.16186/j.cnki.1673-9787.2021010112

Received:2021/01/24

Revised:2021/03/09

Published:2022/07/15

Multi-classification recognition method of arc fault based on convolutional autoencoder network

LIKui 1,2, ZHANGDan 1,2, WANGYao 1,2

1.Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province， Hebei University of Technology，Tianjin  300130，China;2.State Key Laboratory of Reliability and Intelligence of Electrical Equipment，Hebei University of Technology,Tianjin  300130，China

Abstract: Under non-linear load conditions,the current waveform during normal operation has similar charac- teristics as arc faults. The arc fault protection device is prone to malfunction.A multi-class recognition method of arc fault based on convolutional autoencoder network was proposed. A convolutional autoencoder was used to extract arc fault features,the parameters were optimized,and Softmax multi-classifier was used to build the arc fault multi-classification and recognition network model. Experimental results showed that,the arc fault identification accuracy of the proposed method was 99.31%, the identification accuracy of the corresponding load type reached 97.94%. It met the requirements of arc fault identification.

Key words:series arc fault;convolutional autoencoder;Softmax multi-classifier;non-linear load;arc fault recognition

基于卷积自编码网络的故障电弧多分类识别方法_李奎.pdf