Ultra-High Frequency Partial Discharge Signal Recognition in GIS based on Fisher Linear Discriminant Theory

Abstract

Abstract In this paper, dual tree complex wavelet transform (DT-CWT) is used to decompose UHF partial discharge (PD) signal in multi-scale. The optimal decomposition level of complex wavelet is solved. The wavelet energy features of real part and imaginary part of UHF-PD signal under the optimal decomposition scale are extracted. Fisher linear discriminant method is used to select the features of energy features and to identify the PD types. The results show that the optimized real part and imaginary part high frequency wavelet energy features can effectively identify four kinds of typical insulation defects, and the recognition rate can reach to 92.5%. Meanwhile, the optimal complex wavelet energy (OCWEF) feature has better sensitivity and recognition effect in PD type identification.