Abstract：Ultra-high frequency(UHF) method has been widely used in GIS partial discharge(PD) detection, and the feature extraction of UHF PD signal is important for the accurate identification of the GIS internal insulation defect types and guiding the repair work, but it’s still lack of effective feature extraction methods. Therefore, this paper takes advantage of the strict box-shaped spectral of harmonic wavelet and proposes a harmonic wavelet packet transform(HWPT) method for UHF PD feature information extraction. Multi-scale decomposition with HWPT is adopted to the UHF PD signal produced by the four typical discharge models in the laboratory, and the sub-band spectrum aliasing and energy leaks of real wavelet packet decomposition is overcome. Using the energy and complexity difference of the UHF PD signal in different scales, the multi-scale energy and multi-scale sample entropy are extracted as the parameters for pattern recognition, so the time-frequency domain information of the UHF PD signal could be described more accurately. The classification and recognition results with support vector machine shows that this method has better recognition result than the real wavelet packet, and the multi-scale energy and multi-scale sample entropy feature parameters are both able to identify the four kinds of insulation defects effectively.
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2015, Vol. 30 
