Feature Information Extraction of Partial Discharge Signal With Complex Wavelet Transform and Singular Value Decomposition Based on Hankel Matrix
Gas insulated switchgear(GIS) would radiate the ultra-high frequency(UHF) electromagnetic waves to the surroundings when partial discharge(PD) occurs.Effective extraction of the feature information of UHF PD signals could conduct and realize online monitoring and fault diagnosis of GIS.Considering the differences of the distribution and complexity of singular information between the wavelet coefficients of each layer,a novel signal processing method was proposed,which combined dual-tree complex wavelet transform(DT-CWT) with singular value decomposition(SVD).The wavelet magnitude sequences were compressed by applying Biarge-Massart threshold strategy,and a composite matrix by using the magnitude sequences was constructed and transformed by SVD.An optimal complex wavelet decomposition algorithm was proposed based on the relationship between the singular entropy of the matrix and the wavelet decomposition level.Then,the UHF PD signals were processed in the optimal decomposed level,and the compressed magnitude sequences of each high frequency layer were used to construct a Hankel matrix,and the largest singular values and singular entropies of each Hankel matrix were extracted.Finally,the recognition results show that these feature parameters extracted by the new method can identify four kinds of typical insulation defects in GIS effectively,and all the recognition rates have reached 92％ or above.
partial dischargecomplex wavelet transformsingular value decompositionHankel matrixfeature extraction
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