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An improved threshold estimation technique for partial discharge signal denoising using Wavelet Transform

Recent research have shown that the Wavelet Transform (WT) can potentially be used to extract Partial Discharge (PD) signals from severe noise like White noise, Random noise and Discrete Spectral Interferences (DSI). It is important to define that noise is a significant problem in PD detection. Accordingly, the paper mainly deals with denoising of PD signals, based on improved WT techniques namely Translation Invariant Wavelet Transform (TIWT). The improved WT method is distinct from other traditional method called as Fast Fourier Transform (FFT). The TIWT not only remain the edge of the original signal efficiently but also reduce impulsive noise to some extent. Additionally Translation Invariant (TI) Wavelet Transform denoising is used to suppress Pseudo Gibbs phenomenon. In this paper an attempt has been made to review the methodology of denoising the partial discharge signals and shows that the proposed denoising method results are better when compared to other wavelet-based approaches like FFT, wavelet hard thresholding, wavelet soft thresholding, by evaluating five different parameters like, Signal to noise ratio, Cross correlation coefficient, Pulse amplitude distortion, Mean square error, Reduction in noise level.