Study of Cable Partial Discharge Pattern Recognition under Signal Craggy Indeterminate Interference
Discharge faults triggered by different types of defects in cables are affected by signal skewness and cragness,resulting in poor partial discharge signal pattern recognition of cables.For this reason,under the interference of signal craggy indeterminacy,the cable partial discharge pattern recognition method based on convolutional neural network is proposed.Wavelet threshold denoising method is used to preprocess the cable partial discharge signal.Dual-tree complex wavelet transform is applied to extract the partial discharge signal features.By extracting skewness and cragness,the distribution of the signal are characterized to solve the problem of indeterminate interference of the signal craggy.Convolutional neural network is used to train the extracted features,and the features are used as classifiers to complete the cable partial discharge pattern recognition.The test results show that the recognition accurancy of this method is higher than 95％and the recognition time is lower than 5.4 s.The method has a good performance of cable partial discharge pattern recognition after application.
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