Title :
Wavelet de-noising of partial discharge signals based on genetic adaptive threshold estimation
Author :
Li, Jian ; Cheng, Changkui ; Jiang, Tianyan ; Grzybowski, Stanislaw
Author_Institution :
Dept. of High Voltage & Insulation Eng., Chongqing Univ., Chongqing, China
fDate :
4/1/2012 12:00:00 AM
Abstract :
Wavelet shrinkage is efficient for de-noising the partial discharge (PD) detection. An improved wavelet de-noising approach for PD online measurement is presented. The wavelet de-noising approach is based on a genetic adaptive threshold estimation (GATE) scheme. The thresholding functions with continuous derivatives are used for the GATE scheme. A genetic algorithm is used to obtain global optimum thresholds of the GATE, and to improve the robustness and computation speed of the adaptive threshold estimation. De-noising experiments of simulative high-frequency PD signals, actual PD ultra-high-frequency (UHF) signals, and a field detected PD signal are presented. The GATE generates significantly smaller waveform distortion and magnitude errors than the Donoho´s soft threshold estimation.
Keywords :
adaptive estimation; adaptive signal detection; genetic algorithms; partial discharge measurement; signal denoising; Donoho´s soft threshold estimation; GATE scheme; PD UHF signal; PD online measurement; PD signal detection; PD ultrahigh-frequency signal; genetic adaptive threshold estimation scheme; genetic algorithm; global optimum threshold; high-frequency PD signal; magnitude error; partial discharge signal detection; partial discharge signal wavelet denoising; waveform distortion; Equations; Estimation; Logic gates; Mathematical model; Noise reduction; Partial discharges; Signal to noise ratio; Partial discharge; adaptive thresholding; de-noising; genetic algorithm; wavelet shrinkage;
Journal_Title :
Dielectrics and Electrical Insulation, IEEE Transactions on
DOI :
10.1109/TDEI.2012.6180248
