Comparative analysis and optimal wavelet selection of partial discharge de-noising methods in Gas-insulated Substation

Abstract

In Electrical power system network Gas insulated substations (GIS) have high degree of reliability. It is observed that certain defects in GIS systems generate partial discharge (PD) activity before the complete failure of the GIS module. Therefore, partial discharge (PD) detection is required for dielectric diagnostics in GIS. A PD is a localized electrical discharge that partially bridges the insulation between conductors. It causes progressive deterioration of the insulation and eventually leads to sudden damage and failure of the equipment. Measurement and identification of PD signal are of great importance for the safe operation and condition-based maintenance of Gas-insulated Substations (GIS). But the partial discharge signal consists of high level noise that limits the precise diagnoses from partial discharge measurements. Hence, de-noising of partial discharge signals is usually the important task while PD analysis and diagnosis. In this paper we discuss about the various methods for de-noising the partial discharge. The wavelet method based de-noising gives better result than any other method. The four indices correlation coefficient, mean square error, energy loss and signal to noise ratio are used as parameters to find to what extent the signal has de-noised. The simulations are carried out in MATLAB.