Classification of UHF Partial Discharge in GIS Using Optimal Complex Wavelet-based Features

In the field of electrical power engineering, pattern recognition is commonly used for classification of defects caused of partial discharge (PD) in gas insulated substation (GIS) in order to assess the insulated system in time. As phase information can´t be acquired effectively using real wavelet transform for extracting features because of only real coefficients being existed, in this paper, the method of complex wavelet transform (CWT) for decomposing ultra-high frequency (UHF) PD signals is proposed; complex coefficient is constructed by combining the real part and imaginary part of complex wavelet coefficients which can describe PD signal integrally; and the features can be extracted from complex coefficients of each scale; additionally, optimal complex coefficients are selected by comparing Jsum criterion; Finally, PD samples are acquired through large number of experiments, and BP neural network is used to identify the PD source by four typical insulated defects effectively. The results show that the features extracted from the optimal compound coefficient corresponding to the best complex wavelet have wonderful results.