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Wavelet De-Noising for PD UHF Signals Based on Adaptive Thresholding by Genetic Algorithm

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3 Author(s)
Jian Li ; State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Department of High Voltage and Insulation Engineering, College of Electrical Engineering, Chongqing University, Chongqing 400044, China ; Changkui Cheng ; Stanislaw Grzybowski

This paper presents an adaptive wavelet thresholding algorithm for de-noising of ultra-high-frequency (UHF) signals of partial discharges (PD). The wavelet de-nosing algorithm is based on an optimum and adaptive shrinkage scheme. A class of shrinkage functions with continuous derivatives and a genetic algorithm are used for the adaptive shrinkage scheme. The genetic algorithm is helpful to obtain global optimum thresholds and to reduce much time wasted by the adaptive searching computation. The de-noising results of PD UHF signals embedded in white noises are presented. The PD UHF signals denoised by the adaptive wavelet thresholding algorithm have smaller distortion in waveform than the signals de-noised by the soft thresholding algorithms.

Published in:

2008 Annual Report Conference on Electrical Insulation and Dielectric Phenomena

Date of Conference:

26-29 Oct. 2008