A new group of image features derived from two-dimensional linear discriminant analysis for partial discharge pattern recognition

Partial discharge (PD) diagnosis is confirmed to be one of the most effective tools for assessing the health condition of power equipment. Classification and recognition of the measured PD data provide the insulation defects information which facilitate the condition diagnosis of electrical apparatus. This paper presents a new group of image features for partial discharge classification, where the gray images are formed to represent different PD defects. The PD gray images are decomposed into various vectors by two-dimensional linear discriminant analysis (2DLDA), where 9 representative parameters are extracted from each image vector. Finally, fuzzy k-nearest neighbor classifier (FkNNC), multi-class support vector machine (MC-SVM) and back-propagation neural network (BPNN) are respectively employed for PD classification. 419 diversified samples of PD data acquired from typically artificial defect models of oil/pressboard insulation, where the defect size, applied voltage and insulation aging are taken into account, are employed for algorithm validation. The recognition results of 419 PD samples show that the defects are well identified by the proposed 2DLDA features with high accuracy. In addition, the significant increments of average recognition accuracies are obtained by different classifiers compared with the phase-resolved partial discharge (PRPD) features in previous works. The obtained results indicate that the proposed 2DLDA features are potentially effective and reliable in recognizing different PD sources and may be considered as an improved PD recognition tool when compared with the intensively used PRPD features.