Partial discharge pattern recognition based on S transform and two-directional 2DPCA

A new feature extraction method is proposed to recognize different types of partial discharge (PD) signals. Firstly,four typical categories of PD artificial defect models are made and S transform (ST) is employed to obtain a time-frequency representation of the recorded UHF signals. Then,two-directional two-dimensional principal component analysis ((2D) 2PCA) is applied to compress the ST amplitude (STA) matrix to extract features. Finally,support vector machine (SVM) combined with particle swarm optimization (PSO) algorithm is introduced to accomplish the recognition of experimental samples. Classification results demonstrate that the average recognition rate of (10,5) combination is the highest while the one of (5,5) combination is the lowest among four kinds of feature dimension combinations. Moreover,PSO can obviously improve the classification performance of SVM. Specifically,all the average recognition rates of PSO-SVM are higher than 94.43%and the maximum value comes to 97.67%. Therefore,the feature sets extracted by ST and (2D) 2PCA can not only achieve dramatic dimension reduction,but also retain the major information of original data. It is proved that the proposed algorithm can obtain ideal results in PD pattern recognition.