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dc.contributor.authorRamirez García, Ibai
dc.contributor.authorAizpurua Unanue, Jose Ignacio
dc.contributor.otherLasa, Iker
dc.contributor.otherdel Rio, Luis
dc.date.accessioned2024-03-25T09:38:11Z
dc.date.available2024-03-25T09:38:11Z
dc.date.issued2024
dc.identifier.issn1873-6769en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=174462en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/6306
dc.description.abstractThe increased penetration of renewable energy sources (RESs) as an effective mechanism to reduce carbon emissions leads to an increased weather dependency for power and energy systems. This has created dynamic operation and degradation phenomena, which affect the lifetime estimation of the assets operated with RESs. For the reliable and efficient operation of RES it is crucial to monitor the health of its constituent components and feature selection is a crucial step for building robust and accurate health monitoring approaches. In this context, this paper presents a probabilistic feature selection approach, which probabilistically weights and selects features through a heuristic and iterative process for an improved asset lifetime estimation. Power transformers are key power grid assets and they are used to demonstrate the validity and impact of the proposed approach. The approach is tested on two different photovoltaic power plants operated in Spain and Australia. Results consistently show that the proposed feature-selection approach reduces the prediction error and consistently selects relevant features. The approach has been applied to transformer lifetime estimation, but it can be generally applied to assist in the lifetime estimation of other components operated in RESs. Part of the studies presented here as well as source codes are all open-source under the GitHub repository https://github.com/iramirezg/FeatureSelection.en
dc.language.isoengen
dc.publisherElsevieren
dc.rights© 2024 Elsevieren
dc.subjectPrognosticsen
dc.subjectDegradationen
dc.subjectFeature selectionen
dc.subjectMachine learningen
dc.subjectTransformeren
dc.titleProbabilistic feature selection for improved asset lifetime estimation in renewables. Application to transformers in photovoltaic power plantsen
dcterms.accessRightshttp://purl.org/coar/access_right/c_16ecen
dcterms.sourceEngineering Applications of Artificial Intelligenceen
local.contributor.groupTeoría de la señal y comunicacioneses
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.1016/j.engappai.2023.107841en
local.embargo.enddate2026-01-31
local.contributor.otherinstitutionhttps://ror.org/01cc3fy72en
local.contributor.otherinstitutionOrmazabalen
local.source.detailsVol. 131. N. art. 107841, 2024
oaire.format.mimetypeapplication/pdfen
oaire.file$DSPACE\assetstoreen
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501en
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aaen
oaire.funderNameGobierno de Españaen
oaire.funderNameGobierno Vascoen
oaire.funderNameGobierno de Españaen
oaire.funderIdentifierhttps://ror.org/038jjxj40 / http://data.crossref.org/fundingdata/funder/10.13039/501100010198en
oaire.funderIdentifierhttps://ror.org/00pz2fp31 / http://data.crossref.org/fundingdata/funder/10.13039/501100003086en
oaire.funderIdentifierhttps://ror.org/038jjxj40 / http://data.crossref.org/fundingdata/funder/10.13039/501100010198
oaire.fundingStreamConvocatoria 2021. Programa Estatal para Impulsar la Investigación Científico-Técnica y su Transferencia, del Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023en
oaire.fundingStreamIkertalde Convocatoria 2022-2023en
oaire.fundingStreamConvocatoria 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-incorporaciónen
oaire.awardNumberCPP2021-008580en
oaire.awardNumberIT1451-22en
oaire.awardNumberIJC2019-039183-Ien
oaire.awardTitleModelización y Diagnóstico de Transformadores (MODITRANS)en
oaire.awardTitleTeoría de la Señal y Comunicacionesen
oaire.awardTitleSin informaciónen
dc.unesco.campohttp://skos.um.es/unesco6/33en
dc.unesco.disciplinahttp://skos.um.es/unesco6/3322en


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