High-impedance fault identification and classification using a discrete wavelet transform and artificial neural networks

Identification and classification of high-impedance faults (HIFs) in electric-power distribution systems (EPDSs) represent some of the most significant challenges faced by the distribution system operators (DSOs). The recent advances in signal processing and changes in the EPDS regulatory framework have prompted acceleration in the development of advanced methods used for fault identification and classification in EPDS. The paper presents a method for identification and classification of HIFs in medium-voltage (MV) EPDSs, based on the Discrete Wavelet Transform and Artificial Neural Networks. The method was tested on generated signals based on a real EPDS and it was demonstrated that it is capable to accurately detect and classify HIFs in EPDS. The paper contributes to the existing research by developing and testing, on a real EPDS, a HIF-identification and classification method which offers a better performance compared to the currently installed protection devices.