Classification methodology and feature selection to assist fault location in power distribution systems

Abstract

A classification methodology based on Support Vector Machines (SVM) is proposed to locate the faulted zone in power distribution networks. The goal is to reduce the multiple-estimation problem inherent in those methods that use single end measures (in the substation) to estimate the fault location in radial systems. A selection of features or descriptors obtained from voltages and currents measured in the substation are analyzed and used as input of the SVM classifier. Performance of the fault locator having several combinations of these features has been evaluated according to its capability to discriminate between faults in different zones but located at similar distance. An application example illustrates the precision, to locate the faulted zone, obtained with the proposed methodology in simulated framework. The proposal provides appropriate information for the prevention and opportune attention of faults, requires minimum investment and overcomes the multiple-estimation problem of the classic impedance based methods.