Hybrid transformer prognostics framework for enhanced probabilistic predictions in renewable energy applications

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Aizpurua, Jose Ignacio and Ramirez, Ibai and Lasa, Iker and del Rio, Luis and Ortiz, Alvaro and Stewart, Brian G. (2023) Hybrid transformer prognostics framework for enhanced probabilistic predictions in renewable energy applications. IEEE Transactions on Power Delivery, 38 (1). pp. 599-609. ISSN 0885-8977 (https://doi.org/10.1109/TPWRD.2022.3203873)

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Abstract

The intermittent nature of renewable energy sources (RESs) hamper their integration to the grid. The stochastic and rapid-changing operation of RES technologies impact on power equipment reliability. Transformers are key integrative assets of the power grid and it is crucial to monitor their health for the reliable integration of RESs. Existing models to transformer lifetime estimation are based on point forecasts or steady-state models. In this context, this article presents a novel hybrid transformer prognostics framework for enhanced probabilistic predictions in RES applications. To this end, physics-based transient thermal models and probabilistic forecasting models are integrated using an error-correction configuration. The thermal prediction model is then embedded within a probabilistic prognostics framework to integrate forecasting estimates within the lifetime model, propagate associated uncertainties and predict the transformer remaining useful life with prediction intervals. Prediction intervals vary for each prediction according to the propagated uncertainty and they inform about the confidence of the model in the predictions. The proposed approach is tested and validated with a floating solar power plant case study. Results show that, from the insulation degradation perspective, there may be room to extend the transformer useful life beyond initial lifetime assumptions.

  • Item type:Article
    ID code:82188
    Dates:
    Date
    Event
    1 February 2023
    Published
    2 September 2022
    Published Online
    31 July 2022
    Accepted
    18 March 2022
    Submitted
    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:02 Sep 2022 11:27
    Last modified:12 Dec 2024 13:43
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/82188
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