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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Abstract

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 AC grid can contribute to time-variant non-linearity, which is undesirable to the development of data-driven method. A novel real-time scheme is thus proposed to exclude such component 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)
PublisherIEEE
Pages1129-1134
Number of pages6
ISBN (Electronic)9781728156354
ISBN (Print)9781728156361
DOIs
Publication statusPublished - 30 Jul 2020

Publication series

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

Keywords

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

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  • Cite this

    Javed, W., & Chen, D. (2020). Data-driven fault localization of a DC microgrid with refined data input. In 2020 IEEE 29th International Symposium on Industrial Electronics (ISIE) (pp. 1129-1134). IEEE. https://doi.org/10.1109/ISIE45063.2020.9152378