Data-driven fault localization of a DC microgrid with refined data input

Waqas Javed*, Dong Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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This paper proposes an online fault localization method for low voltage DC microgrids. This method is based on Artificial Neural Network (ANN) and only requires real-time measurements of a local power converter to locate a fault. During a DC fault, the current component fed by the AC grid can contribute to time-variant non-linearity, which is undesirable to the development of the data-driven method. A novel real-time scheme is thus proposed to exclude such components from DC fault current. The principle of the scheme is introduced and illustrated with time-domain analysis. The effectiveness is verified by case studies of locating a DC fault in a radial DC network fed by a 3-phase voltage source converter.
Original languageEnglish
Title of host publication2020 IEEE 29th International Symposium on Industrial Electronics (ISIE)
Number of pages6
ISBN (Electronic)9781728156354
ISBN (Print)9781728156361
Publication statusPublished - 30 Jul 2020

Publication series

ISSN (Print)2163-5137
ISSN (Electronic)2163-5145


  • Low-voltage DC Microgrid
  • Fault localization
  • Data-driven


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