Feature parameters extraction of gis partial discharge signal with multifractal detrended fluctuation analysis


Ultra-high frequency (UHF) method is widely utilized in gas-insulated switchgear (GIS) partial discharge (PD) online monitoring because this method has excellent anti-interference ability and high sensitivity. GIS PD pattern recognition is based on effective options acquired from UHF PD signals. Therefore, this paper proposes a new feature extraction methodology that's primarily based on multifractal detrended fluctuation analysis (MFDFA). UHF PD signals of 4 typical GIS discharge models that were collected during a laboratory were analyzed. Additionally, the multifractal feature of these signals was investigated. The one-scale shortcoming of ancient detrended fluctuation analysis and its sensitivity to interference information trends were overcame. Thus, the proposed methodology was ready to effectively characterized the multi-scaling behavior and nonlinear characteristics of UHF PD signals. With the utilization of the shape and distribution difference of the multifractal spectrum, seven feature parameters with clear physical meanings were extracted as feature amount for pattern recognition and input to the support vector machine for classification. Results showed that the feature extraction methodology based mostly on MFDFA may effectively determine four kinds of insulation defects even with robust background noise. The average recognition rate exceeded 90percent, which is significantly better than that of wavelet packet-based feature extraction.

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