Automated distribution network fault cause identification with advanced similarity metrics

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Jiang, Xu and Stephen, Bruce and McArthur, Stephen (2020) Automated distribution network fault cause identification with advanced similarity metrics. IEEE Transactions on Power Delivery. ISSN 0885-8977 (https://doi.org/10.1109/TPWRD.2020.2993144)

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Abstract

Distribution network monitoring has the potential to improve service levels by reporting the origin of fault events and informing the nature of remedial action. To achieve this practically, intelligent systems to automatically recognize the cause of network faults could provide a data driven solution, however, these usually require a large amount of examples to learn from, making their implementation burdensome. Furthermore, the choice of input to such a system in order to make accurate classifications is not always clear. In response to this challenge, this paper contributes a means of using minimal amounts of historical fault data to infer fault cause from substation current data through a novel structural similarity metric applied to the associated power quality waveform. This approach is demonstrated along with disturbance context similarity assessment on an industrially relevant benchmark data set where it is shown to provide an improvement in classification accuracy over comparable techniques.

ORCID iDs

Jiang, Xu ORCID logoORCID: https://orcid.org/0000-0001-9123-9911, Stephen, Bruce ORCID logoORCID: https://orcid.org/0000-0001-7502-8129 and McArthur, Stephen ORCID logoORCID: https://orcid.org/0000-0003-1312-8874;
  • Item type:Article
    ID code:72358
    Dates:
    Date
    Event
    7 May 2020
    Published
    7 May 2020
    Published Online
    30 April 2020
    Accepted
    Notes:© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:13 May 2020 15:45
    Last modified:18 Dec 2024 01:26
    URI:https://strathprints.strath.ac.uk/id/eprint/72358
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