A novel algorithm for denoising of simulated partial discharge signals using Adaptive wavelet thresholding methods

Partial Discharge (PD) signal denoising is very significant in analyzing its characteristics and its effect on high voltage insulation equipments. Mainly, the PD information is lost in the presence of various noises. The wavelet transform provides a better platform for PD signal pre and post processing. The wavelet adaptive thresholding denoising techniques provides a better method to reduce noise. This paper adopts the various adaptive thresholding techniques such as VisuShrink, SureShrink, combination of the two called Heursure and the minimax thresholding which are broadly classified as Global and Local thresholding methods. The algorithm presents the comparative analysis for the selection of optimal wavelet. Once the optimal mother wavelet is chosen, selection of best thresholding rule is identified by comparing the values of signal to noise ratio (SNR), mean square error (MSE) and root mean square error (RMSE) of all the techniques. The algorithm also presents the comparison between Hard and Soft thresholding. It is shown that the soft thresholding is best suited to remove the noise, but weak in preserving the edges and the hard thresholding is best suited for preserving the edges, but weak in denoising the signal. The simulated damped exponential pulse (DEP) and damped oscillatory pulse (DOP) has been used to check the performance of the algorithm.