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dc.contributor.authorAizpurua Unanue, Jose Ignacio
dc.contributor.authorPenalba, Markel
dc.contributor.authorBarrenetxea, Manex
dc.contributor.authorMuxika Olasagasti, Eñaut
dc.contributor.otherStewart, Brian G.
dc.contributor.otherMcArthur, Stephen D.J.
dc.contributor.otherRingwood, John V.
dc.date.accessioned2022-09-16T09:34:03Z
dc.date.available2022-09-16T09:34:03Z
dc.date.issued2022
dc.identifier.issn0951-8320en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=168000en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/5675
dc.description.abstractThe 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.en
dc.description.sponsorshipGobierno de Españaes
dc.description.sponsorshipGobierno Vascoes
dc.language.isoengen
dc.publisherElsevier Ltd.en
dc.rights© 2022 Elsevier Ltd. All rights reserveden
dc.subjectcondition monitoringen
dc.subjectProbabilistic forecastingen
dc.subjectTransformeren
dc.subjectPrognosticsen
dc.subjectuncertaintyen
dc.titleProbabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case studyen
dcterms.accessRightsen
dcterms.accessRightshttp://purl.org/coar/access_right/c_f1cf
dcterms.sourceReliability Engineering & System Safetyen
local.contributor.groupTeoría de la señal y comunicacioneses
local.contributor.groupSistemas electrónicos de potencia aplicados al control de la energía eléctricaes
local.contributor.groupMecánica de fluidoses
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.1016/j.ress.2022.108676en
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.enddate2024-10-31
local.contributor.otherinstitutionhttps://ror.org/01cc3fy72es
local.contributor.otherinstitutionhttps://ror.org/00n3w3b69es
local.contributor.otherinstitutionhttps://ror.org/048nfjm95es
local.source.detailsVol. 226. October, 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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