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

Waqas Javed; Dong Chen

doi:10.1109/ISIE45063.2020.9152378

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

Waqas Javed^*, Dong Chen

^*Corresponding author for this work

Electrical Power Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

22 Downloads (Pure)

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 language	English
Title of host publication	2020 IEEE 29th International Symposium on Industrial Electronics (ISIE)
Publisher	IEEE
Pages	1129-1134
Number of pages	6
ISBN (Electronic)	9781728156354
ISBN (Print)	9781728156361
DOIs	https://doi.org/10.1109/ISIE45063.2020.9152378
Publication status	Published - 30 Jul 2020

Publication series

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

Keywords

Low-voltage DC Microgrid
Fault localization
Data-driven

Documents and Links

10.1109/ISIE45063.2020.9152378

Javed, W. & Chen, D. (2020) Data-driven fault localization of a DC microgrid with refined data input
Copyright © 2020 IEEE. This is a post-peer review, pre-copy edited version of the article and may differ slightly from the final published version of the work. This work is made available in line with the publisher policy. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Accepted author manuscript, 1.41 MB

Fingerprint Dive into the research topics of 'Data-driven fault localization of a DC microgrid with refined data input'. Together they form a unique fingerprint.

Cite this

@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)",

}

TY - GEN

T1 - Data-driven fault localization of a DC microgrid with refined data input

AU - Javed, Waqas

AU - Chen, Dong

N1 - 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"

PY - 2020/7/30

Y1 - 2020/7/30

N2 - 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.

AB - 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.

KW - Low-voltage DC Microgrid

KW - Fault localization

KW - Data-driven

U2 - 10.1109/ISIE45063.2020.9152378

DO - 10.1109/ISIE45063.2020.9152378

M3 - Conference contribution

SN - 9781728156361

SP - 1129

EP - 1134

BT - 2020 IEEE 29th International Symposium on Industrial Electronics (ISIE)

PB - IEEE

ER -