Adaptive power transformer lifetime predictions through machine learning and uncertainty modelling in nuclear power plants

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Aizpurua, Jose Ignacio and McArthur, Stephen D. J. and Stewart, Brian G. and Lambert, Brandon and Cross, James G. and Catterson, Victoria M. (2019) Adaptive power transformer lifetime predictions through machine learning and uncertainty modelling in nuclear power plants. IEEE Transactions on Industrial Electronics, 66 (6). pp. 4726-4737. ISSN 0278-0046 (https://doi.org/10.1109/TIE.2018.2860532)

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Abstract

The remaining useful life (RUL) of transformer insulation paper is largely determined by the winding hot-spot temperature (HST). Frequently the HST is not directly monitored and it is inferred from other measurements. However, measurement errors affect prediction models and if uncertain variables are not taken into account this can lead to incorrect maintenance decisions. Additionally, existing analytic models for HST calculation are not always accurate because they cannot generalize the properties of transformers operating in different contexts. In this context, this paper presents a novel transformer condition assessment approach integrating uncertainty modeling, data-driven forecasting models and model-based experimental models to increase the prediction accuracy and handle uncertainty. The proposed approach quantifies the effect of measurement errors on transformer RUL predictions and confirms that temperature and load measurement errors affect the RUL estimation. Forecasting results show that the extreme gradient boosting (XGB) algorithm best captures the non-linearities of the thermal model and improves the prediction accuracy amongst a number of forecasting approaches. Accordingly, the XGB model is integrated with experimental models in a Particle Filtering framework to improve thermal modelling and RUL prediction tasks. Models are tested and validated using a real dataset from a power transformer operating in a nuclear power plant.

ORCID iDs

Aizpurua, Jose Ignacio ORCID logoORCID: https://orcid.org/0000-0002-8653-6011, McArthur, Stephen D. J. ORCID logoORCID: https://orcid.org/0000-0003-1312-8874, Stewart, Brian G., Lambert, Brandon, Cross, James G. and Catterson, Victoria M. ORCID logoORCID: https://orcid.org/0000-0003-3455-803X;
  • Item type:Article
    ID code:64723
    Dates:
    Date
    Event
    30 June 2019
    Published
    1 August 2018
    Published Online
    3 July 2018
    Accepted
    Notes:© 2018 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:09 Jul 2018 11:17
    Last modified:17 Dec 2024 15:28
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/64723
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