Feature Extraction Method for Partial Discharge in GIS Based on KAZE and Distribution Entropy in Polar Coordinates
Accurate identification of partial discharge(PD)defect of gas-insulated switchgear(GIS)is very important for the state assessment and fault diagnosis of power equipment,to solve the problem of insufficient accuracy of feature extraction in PD pattern recognition,a feature extraction method based on KAZE and distribution entropy in polar coordinates is proposed.First,non-subsampled contourlet transform(NSCT)is used to obtain the photoelectric fusion images containing more information.Then,the KAZE method is used to extract the typical feature points,and the feature points are diverged to the extreme coordinates according to phase,amplitude,and scale information.The subregion distribution entropy is extracted to form the feature vector.Finally,the feature information is brought into the long short-term memory network(LSTM)optimized by an adaptive enhancement algorithm(Adaboost)for pattern recognition verification,which will be compared by the KAZE method,statistical parameter method,and convolutional neural networks(CNN).The results show that the feature extraction method proposed in this paper can achieve a high recognition rate of up to 91％under different training set distributions,which is 8.8％and 4.4％higher than the statistical parameter method and CNN.This method can provide a reference for improving the accuracy of GIS PD feature extraction.
gas insulated switchgearpartial dischargeKAZEimage fusiondistribution entropypattern recognition
万方数据
维普
