Adaptive Wavelet De-noising Algorithm using Absolute Difference Optimization Technique for Partial Discharge Signal

Abstract

Discrete Wavelet Transform (DWT) de-noising method is widely used for one-dimension partial discharge (PD) signals measured from medium voltage underground cable. However, DWT de-noising has several drawbacks that prevent the DWT de-noising from improving its de-noising effectiveness In DWT de-noising, the two most important parameters are decomposition level and mother wavelet. The aforementioned parameters must be varied according to the noise level in the measured PD signal in order to effectively suppress the noise of the measured PD signal. In this paper, an adaptive DWT de-noising algorithm based on the Absolute Difference Optimizing (ADO) technique is presented to effectively suppress the varying noise levels in measured PD signal. First, the measured PD signal will be de-noised using a Daubechies 3 (db3) mother wavelet and a DWT decomposition level ranging from 1 to 10. Second, the de-noised PD signal will be subjected to the ADO technique. The sum of the absolute difference of local maxima in the de-noised PD signal will be used as an indicator to select the best decomposition level for the de-noised PD signal. Finally, the best-selected de-noised PD signal by using the ADO technique will be used to estimate the PD location on the underground cable. The results of PD location error using the ADO technique and normal DWT de-noising will be compared. The findings show that the ADO-based adaptive DWT de-noising algorithm significantly improved the de-noising process of the measured PD signal.