Partial Discharge Signal Denoising Based on Singular Value Decomposition and Empirical Wavelet Transform
Jun Zhong, Xiaowen Bi, Qin Shu, Minwei Chen, Dianbo Zhou, Dakun Zhang
Abstract
Online partial discharge (PD) monitoring is an important means to detect insulation deterioration. However, it is difficult to extract the PD signal due to various interferences in the field. Noisy PD signal is used to judge the status of insulation, which would affect the conclusion; therefore, denoising PD signal is a major task in online PD monitoring. Common methods for PD denoising include the empirical mode decomposition (EMD) and wavelet transform; however, the denoising results are highly dependent on the modal aliasing, the selection of mother wavelets, and decomposition levels. This article proposes a method to solve these problems. This method uses traditionally singular value transform [singular value decomposition (SVD)] to reconstruct narrowband interference and remove it. Next, the empirical wavelet transform (EWT) is carried out for the PD signal that has residual white noise. Then, the noisy signal is decomposed into several modes corresponding to each spectrum segment. The $3~\sigma $ principle is used to denoise the modes with large kurtosis, and the modes are combined into a reference signal. The start-end positions of PD signal are then obtained from the reference signal. Finally, the PD signal is obtained by time-domain denoising. The results from both simulated and actual field detection signals show the excellent performance of this method.