A Fractional Fourier Transform-Based Approach for Gas-Insulated Switchgear Partial Discharge Recognition

Abstract

Partial discharge (PD) is very likely to deteriorate the insulating materials in power facilities. For this sake, PD detection and pattern recognition are treated as an effective approach to diagnosing the high-voltage insulation of power systems. A novel PD feature extraction approach for defect pattern recognition of a gas-insulated switchgear (GIS) is developed and then presented in this paper, using fractional Fourier transform (FrFT). A PD detector is utilized to measure the PD signal of the GIS using an ultra high frequency sensor, which is first transformed to its FrFT as the order ranges from 0 to 1. A 3D characteristic spectrum is then constructed, according to which the fractal parameters are extracted as the constituents of the cluster domains of defect types. Extension matter-element models of the PD defect types are then established according to the experimentally derived PD features. Subsequently, the PD defect type can be directly identified using the correlation degree between a tested pattern and the matter-element models. The pattern recognition performance is investigated on 80 sets of PD patterns, and the performance of the proposed approach is demonstrated accordingly. High accuracy, accompanied with high tolerance, was reached in the presence of noise interference using FrFT.