Pulse Analysis Algorithms for Distinguishing Discharge Sources using Different Types of Sensors

It is very important to discriminate noises such as air corona in measuring on-site partial discharges (PD). Therefore, in order to investigate the possibility of separating PD and noises through the pulse wave shape analysis (PWA), pulse wave shapes measured by a resistive sensor and a high frequency current transformer (HFCT) have been analyzed and PWA were performed. For the purpose, the high frequency partial discharge (HFPD) detection and PWA system has been developed. Also void discharges and air corona were adopted as the artificial defect and noises, respectively. For the PWA algorithm, two kinds of methods such as TF-KK (Kurtosis of time domain and frequency domain) and MMR-S (the ratio of max/min value and the summation of the captured data) were compared. As a result, the developed system in this study showed good ability of distinguishing small void discharges from very large noises such as big air corona and ground floating discharges, which implies that the developed HFPD diagnostic system with the PWA algorithm has been proved to have very good performance of eliminating noises