A Gaussian process based fleet lifetime predictor model for unmonitored power network assets

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Jiang, Xu and Stephen, Bruce and Chandarasupsang, Tirapot and McArthur, Stephen D.J. and Stewart, Brian G. (2023) A Gaussian process based fleet lifetime predictor model for unmonitored power network assets. IEEE Transactions on Power Delivery, 38 (2). pp. 979-987. ISSN 0885-8977 (https://doi.org/10.1109/tpwrd.2022.3203161)

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Abstract

This paper proposes the use of Gaussian Process Regression to automatically identify relevant predictor variables in a formulation of a remaining useful life model for unmonitored, low value power network assets. Reclosers are used as a proxy for evaluating the efficacy of this method. Distribution network reclosers are typically high-volume assets without on-line monitoring, leading to an insufficient understanding of which factors drive their failures. The ubiquity of reclosers, and their lack of monitoring, prevents the tracking of their individual remaining life, and, confirms their use in validating the proposed process. As an alternative to monitoring, periodic inspection data is used to evaluate asset risk level, which is then used in a predictive model of remaining useful life. Inspection data is often variable in quality with a number of features missing from records. Accordingly, missing inputs are imputed by the proposed process using samples drawn from an advanced form of joint distribution learned from test records and reduced to its conditional form. This work is validated on operational data provided by a regional distribution network operator, but conceptually is applicable to unmonitored fleets of assets of any power network.

ORCID iDs

Jiang, Xu ORCID logoORCID: https://orcid.org/0000-0001-9123-9911, Stephen, Bruce ORCID logoORCID: https://orcid.org/0000-0001-7502-8129, Chandarasupsang, Tirapot, McArthur, Stephen D.J. ORCID logoORCID: https://orcid.org/0000-0003-1312-8874 and Stewart, Brian G.;
  • Item type:Article
    ID code:82342
    Dates:
    Date
    Event
    1 April 2023
    Published
    31 August 2022
    Published Online
    31 August 2022
    Accepted
    Notes:© 2022 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:14 Sep 2022 09:51
    Last modified:18 Dec 2024 01:33
    URI:https://strathprints.strath.ac.uk/id/eprint/82342
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