MURAL - Maynooth University Research Archive Library

    Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case study

    Aizpurua, J.I. and Stewart, B.G. and McArthur, S.D.J. and Penalba, M. and Barrenetxea, M. and Muxika, E. and Ringwood, John V. (2022) Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case study. Reliability Engineering and System Safety, 226. pp. 1-13. ISSN 0951-8320

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    The energy transition towards resilient and sustainable power plants requires moving from periodic health assessment to condition-based lifetime planning, which in turn, creates new challenges and opportunities for health estimation and prediction. Probabilistic forecasting models are being widely employed to predict the likely evolution of power grid parameters, such as weather prediction models and probabilistic load forecasting models, that precisely impact on the health state of power and energy components. These models synthesize forecasting knowledge and associated uncertainty information, and their integration within asset management practice would improve lifetime estimation under uncertainty through uncertainty-aware probabilistic predictions. Accordingly, this paper presents a probabilistic prognostics method for lifetime planning under uncertainty integrating data-driven probabilistic forecasting models with expert-knowledge based Bayesian filtering methods. The proposed concepts are applied and validated with power transformers operated in two different power generation systems and obtained results confirm that the proposed probabilistic transformer lifetime estimate aids in the decision-making process with informative lifetime distributions and associated confidence intervals.

    Item Type: Article
    Keywords: Condition monitoring; Probabilistic forecasting; Transformer; Prognostics; Uncertainty;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Faculty of Science and Engineering > Research Institutes > Centre for Ocean Energy Research
    Item ID: 16315
    Identification Number:
    Depositing User: Professor John Ringwood
    Date Deposited: 15 Jul 2022 10:21
    Journal or Publication Title: Reliability Engineering and System Safety
    Publisher: Elsevier
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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