Automated fault analysis and diagnosis using high-frequency and maintenance data from distribution networks

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Jiang, Xu and Stephen, Bruce and McArthur, Stephen D. J.; (2019) Automated fault analysis and diagnosis using high-frequency and maintenance data from distribution networks. In: 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe). IEEE, ROM. ISBN 9781538682180 (https://doi.org/10.1109/ISGTEurope.2019.8905745)

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Abstract

Fault analysis based on high-resolution data acquisition is growing in use as it offers a more complete picture of faults which provides an opportunity to deal with failures more effectively. However, with increased volume of data collected, it becomes impossible for engineers to interpret every fault instance. A machine learning approach to classification should be the solution to this, but it is time-consuming to manually label faults for training and validation making data-driven approaches impossible to transfer into practical implementation. A solution to this is to unify fault analysis with maintenance report analysis to automate the generation of training labels. This paper outlines how a fully automatic fault detection and diagnostic approach based around power quality waveform analysis can be used to improve situational awareness on distribution networks. The methodology is illustrated using operational case study data and realistic simulations to demonstrate the diagnostic functionality as well as the practical benefit. In particular, classification accuracy is shown to approach that of expert labelled fault data.

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 D. J. ORCID logoORCID: https://orcid.org/0000-0003-1312-8874;
  • Item type:Book Section
    ID code:69746
    Dates:
    Date
    Event
    29 September 2019
    Published
    1 June 2019
    Accepted
    Notes:© 2019 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:11 Sep 2019 09:41
    Last modified:28 Nov 2024 01:31
    URI:https://strathprints.strath.ac.uk/id/eprint/69746
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