Knowledge-Based Fault Diagnosis for a Distribution System with High PV Penetration
Shuva Paul, Santiago Grijalva, Miguel Jimenez Aparicio, Matthew J. Reno
Abstract
Identifying the location of faults in a fast and accurate manner is critical for effective protection and restoration of distribution networks. This paper describes an efficient method for detecting, localizing, and classifying faults using advanced signal processing and machine learning tools. The method uses an Isolation Forest technique to detect the fault. Then Continuous Wavelet Transform (CWT) is used to analyze the traveling waves produced by the faults. The CWT coefficients of the current signals at the time of arrival of the traveling wave present unique characteristics for different fault types and locations. These CWT coefficients are fed into a Convolutional Neural Network (CNN) to train and classify fault events. The results show that for multiple fault scenarios and solar PV conditions, the method is able to determine the fault type and location with high accuracy.