1. Department of Electrical Engineering Shanghai Jiao Tong University Shanghai 200240 China; 2. Electric Power Research Institute of State Grid Zhejiang Electric Power Co. Ltd Hangzhou 310014 China
Abstract Present partial discharge (PD) pattern recognition of DC cross-linked polyethylene (XLPE) cables has some limitations on the feature extraction of strong random signals. In order to solve this problem, this paper proposes a self-adaptive pattern recognition based on convolutional neural network (CNN). Convolutional architecture for fast feature embedding (CAFFE) was used to train the CNN. First, PD signals of four typical insulation defects were collected as the input samples of CAFFE. Then, the training cycles were iterated by taking self-adaptive convolution kernels to extract features, pooling layers to map features, nonlinear multi-classifiers to classify different types, until the CAFFE network was completely trained. After comparison of different parameters of solver, network structures and numbers of training samples, it is found that pattern recognition framework using the modified Alexnet network and attenuation learning rate method has the highest accuracy of 91.32%. Moreover, it has at least 8.97% improvement compared with traditional methods. The powerful self-adaptive learning capabilities of the new method provide a new idea for pattern recognition of DC cable fault diagnosis.
Zhu Yufeng,Xu Yongpeng,Chen Xiaoxin等. Pattern Recognition of Partial Discharges in DC XLPE Cables Based on Convolutional Neural Network[J]. Transactions of China Electrotechnical Society, 2020, 35(3): 659-668.
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2020, Vol. 35 
