Decision tree‐based method for optimum decomposition level determination in wavelet transform for noise reduction of partial discharge signals

Abstract

Partial discharge (PD) monitoring in high-voltage equipment is one of the effective methods for assessment of its insulation strength. To do this, noise reduction is one of the essential processes on measured PD signals. One of the most popular tools employed for PD de-noising is wavelet transform. To exploit this transformation, three main parameters, including ‘mother wavelet’, ‘number of decomposition level’, and ‘thresholding procedure’, should be assigned. In this study, a novel and also more accurate method for the determination of required decomposition level is suggested. The proposed method employs a decision tree which takes the pattern of energy spectral density of PD signals in the frequency domain and delivers the optimum decomposition level for de-noising by wavelet transform. The results, as compared with others, show the superiority of the proposed method in noise reduction of PD signals, both for simulations and field measured signals.