Application of Fuzzy C-Means Clustering Approach and Genetic Algorithm to Partial Discharge Pattern Recognition

摘要

The applications of fuzzy c-means (FCM) clustering approach and genetic algorithm (GA) to recognize partial discharge (PD) patterns of high-voltage electrical apparatus are proposed in this paper. The PD patterns are collected by a PD detecting system in the laboratory. Several statistical methods are used on the phase-related distributions in this paper to extract the features for clustering. After the feature extraction procedure, we employ GA for selection of optimal feature combination. Based on the optimal features selected by GA, the PD pattern represented by feature vectors are clustered through the FCM scheme with reasonable discrimination. To verify the proposed approach, experiments were conducted to demonstrate the field-test PD pattern recognition of two kinds of models with artificial defects are purposely created to produce the common PD activities of insulators by using feature vectors of field-test PD patterns. It has been shown that through the features extraction and optimal vector selection procedure, the extracted statistical featuring vectors can significantly reduce the size of the PD pattern database. Also, the FCM based PD pattern recognition scheme is very effective for recognizing the defects of high-voltage electrical apparatus.