Vol. 160, Issue 12, December 2013, pp. 533-540

Abstract: In order to study the ultra high frequency method applied for partial discharge detection in gas insulated switchgear, the typical defects including free particles, metal spikes, floating metals, insulator defects were designed and simulated in the gas insulated switchgear. The ultra high frequency method was applied for detect defects discharge signal, and extracted the statistical operators of the defect characteristics. Applied support vector machine for pattern recognition, and used particle swarm optimization and grid search to optimization support vector machine penalty parameter "c" and kernel function parameter "g". The results show that the ultra high frequency signal of different types of defects in the spectrum would show different characteristics in the ultra high frequency detection; two optimization methods of support vector machine are shown better robustness and generalization ability than the support vector machine in pattern recognition.

Keywords: Gas insulated switchgear (GIS), Partial discharge (PD), Ultra high frequency (UHF), Support vector machine (SVM), Particle swarm optimization (PSO), Grid search (GS).

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