Probabilistic machine learning aided transformer lifetime prediction framework for wind energy systems
Aizpurua, Jose I. and Peña-Alzola, Rafael and Olano, Jon and Ramirez, Ibai and Lasa, Iker and del Rio, Luis and Dragicevic, Tomislav (2023) Probabilistic machine learning aided transformer lifetime prediction framework for wind energy systems. International Journal of Electrical Power and Energy Systems, 153. 109352. ISSN 0142-0615 (https://doi.org/10.1016/j.ijepes.2023.109352)
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Abstract
Accurate lifetime prediction of transformers operated in power grids with renewable energy systems is a challenging task because it requires a large amount of data that is not usually available. In the case of wind energy, this complexity is intensified with the stochastic ageing process influenced by the intermittency of the wind and weather conditions. Existing models make use of detailed power topologies to evaluate transformer stress profiles and associated degradation. However, this modelling approach requires high computational resources and long simulation times. In this context, this paper presents a lifetime prediction model for transformers designed through probabilistic machine learning, thermal modelling and ageing analysis. The proposed model is compared with synthetic wind-to-power detailed simulations of a wind farm and validated with real data. The lifetime prediction is evaluated with different mission profile estimates and results show that the accuracy of the probabilistic machine learning model is very high, with an error of 0.47% for the median value and 80% prediction interval errors within 6%–7% with respect to observations. Moreover, there is a substantial reduction in the simulation time and memory requirements when compared to the synthetic model. A detailed sensitivity analysis demonstrates the influence on transformer ageing of different overloading strategies, thermal constants and the geographic location of the wind farm.
ORCID iDs
Aizpurua, Jose I., Peña-Alzola, Rafael
ORCID: https://orcid.org/0000-0002-2451-6779, Olano, Jon, Ramirez, Ibai, Lasa, Iker, del Rio, Luis and Dragicevic, Tomislav;
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Item type: Article ID code: 93935 Dates: DateEventNovember 2023Published12 July 2023Published Online29 June 2023AcceptedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering
Faculty of Humanities and Social Sciences (HaSS) > Psychological Sciences and HealthDepositing user: Pure Administrator Date deposited: 26 Aug 2025 08:46 Last modified: 02 Sep 2026 02:14 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/93935
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