Detection of High Impedance Faults in Microgrids using Machine Learning

TL;DR

This paper develops machine learning models using wavelet features for differential protection in microgrids, effectively detecting high impedance faults and distinguishing them from external faults to improve reliability.

Contribution

It introduces a novel ML-based differential protection scheme utilizing wavelet features for high impedance fault detection in microgrids, enhancing traditional relay dependability.

Findings

ML models accurately distinguish internal from external faults

Wavelet features improve fault detection reliability

Models maintain effectiveness under CT saturation conditions

Abstract

This article presents differential protection of the distribution line connecting a wind farm in a microgrid. Machine Learning (ML) based models are built using differential features extracted from currents at both ends of the line to assist in relaying decisions. Wavelet coefficients obtained after feature selection from an extensive list of features are used to train the classifiers. Internal faults are distinguished from external faults with CT saturation. The internal faults include the high impedance faults (HIFs) which have very low currents and test the dependability of the conventional relays. The faults are simulated in a 5-bus system in PSCAD/EMTDC. The results show that ML-based models can effectively distinguish faults and other transients and help maintain security and dependability of the microgrid operation.