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Title
Hybrid Transformer Prognostics Framework for Enhanced Probabilistic Predictions in Renewable Energy Applications
Author
Aizpurua Unanue, Jose Ignacio
Perez Ramirez, Ibai Aner
Author (from another institution)
Lasa, Iker
del Rio Etayo, Luis
Ortiz, Álvaro
Stewart, Brian G.
Research Group
Teoría de la señal y comunicaciones
Other institutions
Ormazabal
University of Strathclyde
Version
Postprint
Rights
© 2022 IEEE
Access
Embargoed access
URI
https://hdl.handle.net/20.500.11984/5854
Publisher’s version
https://doi.org/10.1109/TPWRD.2022.3203873
Published at
IEEE Transactions on Power Delivery  September, 2022
Publisher
IEEE
Keywords
Probabilistic logic
Power transformer insulation
Predictive model
uncertainty ... [+]
Probabilistic logic
Power transformer insulation
Predictive model
uncertainty
Forecasting
Estimation
Transient analysis [-]
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. ... [+]
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 paper 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. [-]
xmlui.dri2xhtml.METS-1.0.item-sponsorship
Gobierno de España
xmlui.dri2xhtml.METS-1.0.item-projectID
info:eu-repo/grantAgreement/GE/Convocatoria 2019. Plan Estatal de I+D+I 2017-2020. Subprograma Estatal de Formación y en el Subprograma Estatal de Incorporación, del Programa Estatal de Promoción del Talento y su Empleabilidad. Ayudas Juan de la Cierva-incorporación/IJC2019-039183-I/ES/
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