Analysis of partial discharge signals using digital signal processing techniques

Abstract

Partial discharge (PD) measurements have emerged as a powerful diagnostic tool for condition monitoring of insulation in high voltage equipments. Study of PD patterns reveals the nature and severity of the defects present in the insulation. Nowadays online and onsite PD measurements are preferred to keep the equipment in service while assessing its condition. A major difficulty in such measurements is to extract the PD signal from severe noise and interferences. Various time and frequency domain de-noising techniques are adopted for the extraction of PD signal. Recent research shows that wavelet analysis is a powerful tool in de-noising PD signals. Wavelet analysis can be performed using discrete wavelet transform (DWT) and second generation wavelet transform (SGWT) (also called lifting wavelet transform). In the wavelet analysis based de-noising of PD signals, selection of mother wavelet, maximum decomposition level, and thresholding rule are some of the important issues that affect the de-noising results. Further, time-frequency analysis of PD signal can be performed using S-transform. S-transform is an effective tool for the time-frequency analysis of a signal. This work applies different wavelet based de-noising techniques to five noisy PD signals having different characteristics. Among the five signals four signals are numerically simulated and one is a practical signal. The signals are de-noised using DWT and SGWT based de-noising schemes. The de-noising schemes adopt different types of mother wavelet selection methods, and thresholding rules. Based on the de-noising techniques, varied de-noising results are obtained. A comparative analysis of the de-noising results is made using various de-noising performance indices. Then the time-frequency analysis of the de-noised practical signal is made using S-transform. From the results of the work it emerges that wavelet analysis is a superior tool for the extraction of PD signals. And selection of mother wavelet and thresholding rule for the wavelet based de-noising, depends on the type of signal and the severity of noise and interferences.