Identification of ultra-high-frequency PD signals in gas-insulated switchgear based on moment features considering electromagnetic mode

The feature extraction and pattern recognition techniques are of great importance to assess the insulation condition of gas-insulated switchgear.In this work,the ultra-high-frequency partial discharge(PD)signals generated from four types of typical insulation defects are analysed using S-transform,and the greyscale image in time-frequency representation is divided into five regions according to the cutoff frequencies of TEm1 modes.Then,the three low-order moments of every subregion are extracted and the feature selection is performed based on the J criterion.To confirm the effectiveness of selected moment features after considering the electromagnetic modes,the support vector machine,k-nearest neighbour and particle swarm-optimised extreme learning machine(ELM)are utilised to classify the type of PD,and they achieve the recognition accuracies of 92,88.5 and 95%,respectively.In addition,the results show that the ELM offers good generalisation performance at the fastest learning and testing speeds,thus more suitable for a real-time PD detection.