Adaptive De-Noising for PD Online Monitoring based on Wavelet Transform

White noise is a major noise that influences accuracy of partial discharge (PD) measurements. De-noising with wavelet shrinkage method is efficient for rejection of white noise. Thresholds of wavelet coefficients are key factors in close relation to the distortion and error of a de-noised PD signal. In this paper, a new thresholding function is introduced for estimating the optimal wavelet thresholds of noisy partial discharge signals. Several examples including two typical PD pulses are given. The de-noising results of four typical artificial signals and simulative noisy PD pulses indicate that the distortion degree and magnitude error of signals de-noised by the adaptive thresholding method are smaller than that of signals de-noised by soft thresholding method. An example of a field-detected PD signal shows the method is effective and practical in PD online monitoring