Artificial Neural Network Based Fault Detection and Fault Location in the DC Microgrid

Qingqing Yang, Jianwei Li, Simon Le Blond, Cheng Wang

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)

Abstract

In DC microgrid, power electronic devices may suffer from over current during short circuit faults. Since DC bus
systems cannot sustain high fault currents, suitable protection strategy in DC lines is indispensable. This paper
presents a novel use of artificial neural network (ANN) for fault detection and fault location in a low voltage DC bus
microgrid system. In the proposed scheme, the faults on DC bus can be fast detected and then isolated without deenergizing
the entire system, hence achieving a more reliable DC microgrid. The neural network is trained based on
the different short circuit faults in DC bus to ensure its validity. A microgrid with ring DC bus, which is segmented
into overlapping nodes and linked with circuit breakers, is built in PSCAD/EMTDC to test the performance of the
protection scheme.
Original languageEnglish
Pages (from-to)129-134
Number of pages5
JournalEnergy Procedia
Volume103
Early online date27 Dec 2016
DOIs
Publication statusPublished - Dec 2016

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