@inproceedings{f7ec15aaef8f473a8414ced7243f8dd2,
title = "Data-driven fault localization of a DC microgrid with refined data input",
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.",
keywords = "Low-voltage DC Microgrid, Fault localization, Data-driven",
author = "Waqas Javed and Dong Chen",
note = "Acceptance in SAN AAM: no embargo Has ISSN - REF scope. ET 13/8/20 AAM uploaded between acceptance and earliest publication. Most appropriate exception applied. ET 13/8/20 Best Presentation Award in the special session of {"}Advanced Technologies for DC Microgrid Plug and Play Operations{"}",
year = "2020",
month = jul,
day = "30",
doi = "10.1109/ISIE45063.2020.9152378",
language = "English",
isbn = "9781728156361",
publisher = "IEEE",
pages = "1129--1134",
booktitle = "2020 IEEE 29th International Symposium on Industrial Electronics (ISIE)",
}