Classification of Partial Discharge Sources using Waveform Parameters and Phase-Resolved Partial Discharge Pattern as Input for the Artificial Neural Network

Abstract

Partial discharge (PD) is one of electrical phenomena which might occur in high voltage (HV) equipment and can be used for diagnosing the condition of the equipment. Artificial neural network (ANN) is then utilized to classify PD source in HV equipment. PD measurements were conducted to generate waveform parameters in laboratory using four kinds of artificial PD sources, three kinds of noise sources by three kinds of sensors (transient earth voltage (TEV) sensor, surface current sensor (SCS) and high frequency current transformer (HFCT)). Nine waveform parameters from one PD event were used for training and testing the ANN (ANN_ WP). For further comparison, phase-resolved partial discharge (PRPD) pattern was also generated and used as input data for training and testing the other ANN (ANN_PR). Results reveal that ANN_ WP provides >96% of recognition rate while ANN_PR gives >90% of recognition rate. Furthermore, the ANNs are then tested using new different artificial void defect. The results show that the ANN_ WP predicted new PD data as void defect with 92 % probability while the ANN_PR prediction probability was found 96%. These results indicate that the waveform parameters can be used as an input data for ANN as well as PRPD pattern to provide sufficient accuracy for identifying the PD source. The results suggest a possibility that developed ANNs can be used as a decision-support tool in HV equipment diagnosis by comparing PD data obtained in the field.