A novel fault diagnosis method of power cable based on convolutional probabilistic neural network with discrete wavelet transform and symmetrized dot pattern
Hong‐Wei Sian, Cheng‐Chien Kuo, Shiue‐Der Lu, Menghui Wang
Abstract
Abstract To accurately diagnose the XLPE power cable insulation fault, this research proposed a novel hybrid algorithm combined with Convolutional Probabilistic Neural Network (CPNN) based on Discrete Wavelet Transform (DWT) and Symmetrized Dot Pattern (SDP) analysis. First, it built seven different power cable insulation defect models to measure partial discharge signals of power cable insulation faults. Then, a discrete wavelet transform was used for noise filtering. The time‐domain partial discharge signal was directly converted into the point coordinate feature image of visual polar coordinates by SDP analyses. Finally, the feature image was trained and recognized by CPNN. After the important feature information of the feature‐image was extracted by convolution layer and pooling layer operations, it is applied to the power cable insulation fault state diagnosis system based on the rapid learning and highly parallel computing of Probabilistic Neural Network (PNN). The experimental results proved that the method proposed in this research could accurately diagnose the power cable insulation fault type and the recognition accuracy is higher than 96%. The proposed method has a short detection time and high diagnostic accuracy. This proves that it can be applied to detect the power cable insulation fault type.