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

Abstract

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 TE(m)(1) 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.

Keywords

• feature extraction
• time-frequency analysis
• learning (artificial intelligence)
• pattern recognition
• support vector machines
• gas insulated switchgear
• particle swarm optimisation
• partial discharge measurement
• power engineering computing
• ultra-high-frequency partial discharge signals
• typical insulation defects
• time-frequency representation
• cutoff frequencies
• low-order moments
• feature selection
• selected moment features
• electromagnetic mode
• nearest neighbour
• real-time pd detection
• ultra-high-frequency pd signals
• gas-insulated switchgear
• feature extraction
• pattern recognition techniques
• insulation condition