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dc.contributor.authorAizpurua Unanue, Jose Ignacio
dc.contributor.authorPerez Ramirez, Ibai Aner
dc.contributor.otherLasa, Iker
dc.contributor.otherdel Rio Etayo, Luis
dc.contributor.otherOrtiz, Álvaro
dc.contributor.otherStewart, Brian G.
dc.date.accessioned2022-11-17T13:09:21Z
dc.date.available2022-11-17T13:09:21Z
dc.date.issued2022
dc.identifier.issn1937-4208en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=168392en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/5854
dc.description.abstractThe 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.en
dc.description.sponsorshipGobierno de Españaes
dc.description.sponsorshipGobierno Vasco-Eusko Jaurlaritzaes
dc.language.isoengen
dc.publisherIEEEen
dc.rights© 2022 IEEEen
dc.subjectProbabilistic logicen
dc.subjectPower transformer insulationen
dc.subjectPredictive modelen
dc.subjectuncertaintyen
dc.subjectForecastingen
dc.subjectEstimationen
dc.subjectTransient analysisen
dc.titleHybrid Transformer Prognostics Framework for Enhanced Probabilistic Predictions in Renewable Energy Applicationsen
dcterms.accessRightshttp://purl.org/coar/access_right/c_f1cfen
dcterms.sourceIEEE Transactions on Power Deliveryen
local.contributor.groupTeoría de la señal y comunicacioneses
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.1109/TPWRD.2022.3203873en
local.relation.projectIDinfo: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/en
local.relation.projectIDinfo:eu-repo/grantAgreement/GV/Elkartek 2021/KK-2021-00021/CAPV/Modelización del comportamiento térmico de los transformadores para aplicaciones fotovoltaicas/TRASMOIIen
local.embargo.enddate2024-09-30
local.contributor.otherinstitutionOrmazabales
local.contributor.otherinstitutionhttps://ror.org/00n3w3b69en
local.source.detailsSeptember, 2022en
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501en
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aaen


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