Definition of Features for GIS Partial Discharge Signals and Recognition of Discharge Types Based on Histograms

Different types of partial discharge (PD) cause varying degrees of insulation damage in gas-insulated switchgear (GIS), making accurate recognition of discharge types crucial for the safe and stable operation of GIS. The PD signal analysis method, including feature definition and extraction, is the foundation for recognizing discharge types. This paper first presents some measurement results of four PD types obtained on a GIS experimental platform. By analyzing the measurement results and corresponding physical mechanisms, we proposed two histogram-based features, which are discharge count versus phase histogram and discharge count versus amplitude histogram. To leverage the complementary advantages of these two features, distance-level fusion is achieved based on histogram distance. Following feature fusion, a distance-based k-nearest neighbors (KNN) classifier is used to recognize the four PD types. Compared with traditional feature fusion methods using feature concatenation, distance-level fusion improves recognition accuracy. The proposed PD type recognition method is also compared with two existing methods: one based on statistical features and another based on phase-resolved partial discharge (PRPD) image features. The results show that the proposed method achieves better recognition performance, with an accuracy of 95.8%.