An Adaptive Denoising Algorithm for Online Condition Monitoring of High-Voltage Power Equipment

Abstract

Partial discharge (PD) diagnostic is an effective tool for condition monitoring of the high-voltage equipment that provides an updated status of the dielectric insulation of the components. Reliability of the diagnostics depends on the quality of the PD measurement techniques and the processing of the measured PD data. The online measured data suffer from various inaccuracies caused by external noise from various sources such as power electronic equipment, radio broadband signals and wireless communication, etc. Therefore, extraction of useful data from the on-site measurements is still a challenge. This article presents a discrete wavelet transform (DWT)-based adaptive denoising algorithm and evaluates its performance. Various decisive steps in applying DWT-based denoising on any signal, including selection of mother wavelet, number of levels in multiresolution decomposition and criteria for reconstruction of the denoised signals are taken by the proposed algorithm and vary from one signal to another without a human intervention. Hence, the proposed technique is adaptive. The proposed solution can enhance the accuracy of the PD diagnostic for HV power components.