Partial Discharge Recognition Reliability Considering the Influence of Multi-factors Based on the Two-directional Fuzzy-weighted Two-dimensional Principal Component Analysis Algorithm

In the current work, a new image-oriented feature extraction algorithm is proposed to improve partial discharge recognition accuracy when the multi-factor influences of insulation aging, defect size, and applied voltage are taken into consideration. A fuzzy-weighted method is designed to modify two-dimensional principal component analysis, producing the fuzzy-weighted two-dimensional principal component analysis algorithm, which incorporates samples distribution to the extracted partial discharge features. By synchronously implementing horizontal and vertical fuzzy-weighted two-dimensional principal component analysis, the proposed two-directional fuzzy-weighted two-dimensional principal component analysis algorithm is developed to extract the partial discharge image features. For algorithm testing, 419 diversified partial discharge samples acquired from typically artificial defect models are employed, in which the multi-factor influences of insulation aging, defect size, and applied voltage are taken into account. It is shown that the optimally successful clustering rate of 91.41% is obtained by fuzzy C-means clustering with the variation of three algorithm parameters. The comparisons with phase-resolved partial discharge statistical features and other popular image compression methods based on the support vector machine also confirms the improvement of partial discharge recognition accuracy using the proposed two-directional fuzzy-weighted two-dimensional principal component analysis algorithm.