Electronic Science and Technology ›› 2022, Vol. 35 ›› Issue (7): 7-13.doi: 10.16180/j.cnki.issn1007-7820.2022.07.002

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Partial Discharge Pattern Recognition of Cable Based on CNN-DCGAN under Small Data

SUN Kang1,XUAN Xuyang1,LIU Penghui1,ZHAO Laijun1,LONG Jie2   

  1. 1. School of Electrical Engineering and Automation,Henan Polytechnic University,Jiaozuo 454000,China
    2. Jiaozuo Power Supply Company,State Grid Henan Electric Power Company,Jiaozuo 454000,China
  • Received:2021-01-28 Online:2022-07-15 Published:2022-08-16
  • Supported by:
    Science and Technology Project of Henan(202102210092);Henan Industry-University-Research Cooperation Project(132107000027)


In the process of cable partial discharge pattern recognition, the traditional manual feature extraction relies on the knowledge and experience of specific fields, and the workload of feature selection and optimization is heavy. In view of this problem and to avoid the overfitting problem of the classifier under the unbalanced small sample data of the model, this study presents a partial discharge pattern recognition method based on CNN-DCGAN in the case of small samples. Partial discharge time domain signals are transformed into two-dimensional image information by sliding time window. The DCGANs are constructed, and the data enhancement is carried out on the basis of the original data set. The original data and the enhanced data are taken as the system input. CNN is constructed, and its nonlinear encoder is used to automatically extract partial discharge features, and the feature classification model is trained by Softmax layer. Experimental results show that compared with artificial features, the recognition accuracy of CNN classifier based on automatic feature extraction is improved by 4.18%. Compared with the original data set, the system recognition accuracy based on the sample enhanced data set is improved by 3.175%.

Key words: partial discharge, feature extraction, data augmentation, convolution neural networks, generative adversarial networks, pattern recognition, insulation defect, time domain signal

CLC Number: 

  • TP181