Optimal Energy Features of Partial Discharge Signals in GIS Extracted by Fisher Linear Discriminant

doi:10.11930/j.issn.1004-9649.201806108

Abstract

Abstract: The dual-tree complex wavelet transform (DT-CWT) is adopted to make a multi-scale decomposition of UHF partial discharge (PD) signals, and an optimal algorithm for solving DT-CWT decomposition is proposed. In addition, the optimal complex wavelet energy (OCWE) features are extracted from the high-layer real and imaginary parts of UHF PD signals after decomposed by DT-CWT, and the fisher linear discriminant method is adopted to select the energy features. Finally, the selected features are used for PD type recognition. The results show that the high-layer wavelet energy features can effectively recognize four typical insulation defects in GIS with a recognition accuracy reaching 94.5% or above. It is proved that the OCWE features are more suitable for PD recognition.

Key words: gas insulated switchgear, partial discharge, energy features, fisher linear discriminant, features selection, high voltage measurement technology

CLC Number:

TM835

TIAN Yu, LUO Sha, LI Binbin, HU Yong. Optimal Energy Features of Partial Discharge Signals in GIS Extracted by Fisher Linear Discriminant[J]. Electric Power, 2019, 52(9): 93-101.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.201806108

https://www.electricpower.com.cn/EN/Y2019/V52/I9/93

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