Data mining approach to fault detection for isolated inverter-based microgrids

This study investigates the problem of fault protection in a microgrid containing inverter-based distributed generators (IBDGs). Owing to the low magnitude of short circuit currents generated by IBDGs, traditional protection techniques which relay on current (fuses and overcurrent relays) may fail to protect such networks. This study addresses the problem of finding suitable features derived from local electrical measurements that can be used by statistical classifiers to better discriminate fault events from normal network events. Given a series of simple electrical features, a study of feature selection and data mining techniques is conducted in the context of fault detection in isolated microgrids with IBDGs. Two statistical classifiers are compared and implemented in this framework: Naive Bayes and decision trees. The proposed approach is tested on a facility scale microgrid consisting of three IBDGs.