A new wavelet selection method for partial discharge denoising

Abstract

Partial discharge signals are used to evaluate the insulation condition in several power devices. Their measurements are often heavily contaminated by noise from different sources and thus the use of noise reduction techniques is required. Wavelet shrinkage denoising methods are frequently employed and several wavelet basis selection approaches have been discussed recently in the literature. This paper presents a new method for choosing the wavelet basis, comprising a novel approach for determining the number of wavelet decomposition levels based on the energy spectral density of the signals, and a scale-dependent algorithm for selecting the wavelet functions based on signal to noise ratios computed from the wavelet coefficients. At each decomposition stage, the predominant band of the partial discharge pulse, called the signal band, is identified as the one that has the highest coefficient magnitude. The other band is assumed to contain predominantly noise, and at each level the method selects the wavelet that maximizes the signal to noise ratio. The proposed approach is compared to the correlation based wavelet selection (CBWS) and to the energy based wavelet selection (EBWS) methods for signals simulated and obtained from high voltage equipment, showing better filtering performance for over 70% of the tested signals and a smaller processing time.