Denoising of partial discharge signals in wavelet packets domain

Abstract

Despite advances in the filtering techniques of partial discharge (PD) signals, an acceptable signal to noise ratio (SNR) remains elusive due to the presence of environmental noises. A wavelet packet transform-based method is proposed for effective denoising of on-site partial discharge data from a power generator and a gas-insulated substation. Taking advantage of the statistical properties of white noise, the method uses variance as the criterion to select the best tree. It is shown by comparative studies that the method restores the PD pulse from data with very low SNR. The denoised PD pulses enable more accurate diagnosis of insulation defects in HV equipment.