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 journalArticle

19 Citations (Scopus)
74 Downloads (Pure)

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 de-energizing 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 pages6
JournalEnergy Procedia
Volume103
DOIs
Publication statusPublished - Dec 2016

Bibliographical note

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Keywords

  • Artificial neural networkDC microgrid
  • fault detection
  • fault location
  • short circuit fault

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