Detection of High Impedance Faults in Microgrids using Machine Learning

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.

References in corpus (3)

• Discrimination of Internal Faults and Other Transients in an Interconnected System with Power Transformers and Phase Angle Regulators
• Detection and Classification of Internal Faults in Power Transformers using Tree-based Classifiers
• Intelligent Protection & Classification of Transients in Two-Core Symmetric Phase Angle Regulating Transformers

Cited by in corpus (1)

• Autoregressive Coefficients based Intelligent Protection of Transmission Lines Connected to Type-3 Wind Farms