An Improved Wavelet Spectrum Segmentation Algorithm Based on Spectral Kurtogram for Denoising Partial Discharge Signals

Abstract

Due to complex disturbance environment, partial discharge (PD) monitor signal denoising is a necessary step to judge the state of equipment insulation. In the denoising process, it is difficult to suppress the white noise due to its wide frequency-domain distribution. To overcome these difficulties, most denoising algorithms need to artificially adjust some threshold parameters or select the number of decomposition layers according to the measured PD signals, which leads to the confusion of uncertainty, and the denoising performance depends on the parameter setting experience. Therefore, the denoising effect cannot be guaranteed in general. This article proposes a method without human intervention in choosing the threshold parameters or the decomposition layer numbers. First, the decomposition layers are directly determined from the spectral kurtogram of PD signal to obtain the reference signal. Second, the reference signal is denoised by $3\sigma $ threshold. At the start point obtained from the denoised signal and the end point obtained from the reference signal envelope, the reference signal is segmented in the time domain to obtain the final denoising PD signal. The denoising results of both the simulated and field-measured PD signals show that the proposed method can effectively remove the white noise and recover PD signal more accurately.