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

© The Institution of Engineering and Technology
Received 16/09/2012, Accepted 22/02/2013, Revised 08/02/2013, Published

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.

Inspec keywords: fault diagnosis; power generation faults; data mining; power generation protection; invertors; power engineering computing; decision trees; Bayes methods; distributed power generation

Other keywords: fault events; data mining approach; Naive Bayes trees; isolated inverter-based microgrids; fault detection; IBDG; inverter-based distributed generators; overcurrent relays; fuses; feature selection; electrical features; electrical measurements; fault protection; facility-scale microgrid; decision trees; short circuit currents; statistical classifiers

Subjects: Other topics in statistics; Data handling techniques; Power convertors and power supplies to apparatus; Power system protection; Distributed power generation; Other topics in statistics; Power engineering computing; Combinatorial mathematics; Knowledge engineering techniques; Combinatorial mathematics

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