Denoising of partial discharge signal using rapid sparse decomposition. (3rd October 2016) - Xie, Jun; Wang, Yongqiang; Lv, Fangcheng; Li, Min
Summary: Partial discharge (PD) detection is essential for electrical equipment. Focusing on the noise suppression of PD signal, a PD signal denoising method based on rapid sparse decomposition was proposed. Through the analysis of PD signal time‐frequency characteristics, the PD signal correlated atom and PD signal correlated overcomplete dictionary were designed, which were correlated to the original PD pulse signal while uncorrelated or weak‐correlated to the random noise and discrete spectral interference (DSI) signal. Denoising the noisy PD signal by sparse decomposition based on matching pursuit algorithm and correlation ratio iteration termination condition, only the original PD pulse signal can be expressed by the best atoms extracted from the PD signal correlated overcomplete dictionary; thus, the goal of denoising is achieved. What is more, the searching progress of best atoms was accelerated by quantum genetic algorithm. After finishing the above denoising process, the extracted noiseless pulse signals can be projected onto a Time–Frequency Map (TF Map) to overcome pulse‐shape interference influence. The denoising method presented in this article is applied to the simulated, laboratory measured and field detected PD signals. The results are critical compared with denoising method based on db2 and db8 wavelet transform. The results show that the denoising method of this article is available to precisely suppress PD signal noise jamming and the denoising effect is superior to traditional wavelet methods, which has less amplitude error as well as less waveform destination. Copyright © 2016 John Wiley & Sons, Ltd.