Machine Learning-Based Protection and Fault Identification of 100% Inverter-Based Microgrids

TL;DR

This paper introduces machine learning-based protection methods for 100% inverter-based microgrids, addressing challenges like low fault currents and bidirectional flows, and demonstrating effectiveness through simulation of various fault scenarios.

Contribution

It proposes a decision tree-based protection approach using local measurements for 100% inverter microgrids, a novel focus compared to prior work on mixed-generation systems.

Findings

Effective fault detection and type identification across seven fault types

High accuracy demonstrated in simulation environment

Addresses implementation challenges in inverter-based microgrid protection

Abstract

100% inverter-based renewable units are becoming more prevalent, introducing new challenges in the protection of microgrids that incorporate these resources. This is particularly due to low fault currents and bidirectional flows. Previous work has studied the protection of microgrids with high penetration of inverter-interfaced distributed generators; however, very few have studied the protection of a 100% inverter-based microgrid. This work proposes machine learning (ML)-based protection solutions using local electrical measurements that consider implementation challenges and effectively combine short-circuit fault detection and type identification. A decision tree method is used to analyze a wide range of fault scenarios. PSCAD/EMTDC simulation environment is used to create a dataset for training and testing the proposed method. The effectiveness of the proposed methods is examined under seven distinct fault types, each featuring varying fault resistance, in a 100% inverter-based microgrid consisting of four inverters.