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Defect Diagnosis of EPR Cable Terminal Based on Symmetrized Dot Pattern |
Zhou Lijun1, Liu Cong1, Quan Shengwei1, Cao Weidong1, Xiang Enxin2 |
1. College of Electrical Engineering Southwest Jiaotong University Chengdu 611756 China; 2. Electric Power Research Institute Yunnan Power Grid Co. Ltd Kunming 650217 China |
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Abstract In order to solve the problem that the intelligent diagnosis of the health condition of on-board cables of high-speed electric multiple units (EMU) in China is limited by the short skylight period, a partial discharge (PD) diagnosis method based on symmetrized dot pattern (SDP) for the terminal defects of ehglene propylene rubber (EPR) cables is proposed. Firstly, the partial discharge test platform was built to obtain the partial discharge signal. Then, a method to determine the SDP parameters of the on-board cable partial discharge signals was proposed, and based on SDP transformation, the partial discharge signals of different types of defects were mapped to the polar coordinate system to form SDP images. Finally, three common deep learning networks—convolutional neural network (CNN), stack autoencoder (SAE) and deep belief network (DBN) were compared to extract deep features of SDP images of different types of defects, and Softmax classifier at the end of the network was used to identify them. The results shows that: Aiming at four typical cable defect, DBN network combined with the SDP image effect is best, defect recognition rate reached 96.1%, compared with the traditional diagnosis methods, identification accuracy increase by about 10%, thus verified through deep learning algorithm of adaptive SDP image feature extracting method, can be effectively used in cable defect diagnosis, and it has a good prospect of engineering application.
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Received: 13 April 2021
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