Erregistro soila

dc.rights.licenseAttribution-NonCommercial-4.0 International*
dc.contributor.authorUrmeneta Olmedo, Jon
dc.contributor.authorIzquierdo, Juan
dc.contributor.otherLeturiondo, Urko
dc.date.accessioned2023-01-30T13:35:25Z
dc.date.available2023-01-30T13:35:25Z
dc.date.issued2023
dc.identifier.issn0960-1481en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=171354en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/5976
dc.description.abstractIn the growing wind energy sector, as in other high investment sectors, the need to make assets profitable has put the spotlight on maintenance. Efficient solutions which leverage from condition or performance based maintenance policies have been proposed during the last decades, but the proposed methods generally focus on individual components or stand for specific application areas. This paper aims to contribute to the development of performance based maintenance strategies within the wind energy sector by providing a condition monitoring based generic methodology for wind turbine performance assessment at system level. The proposed methodology is based on the detection of critical periods in which low performance is detected repeatedly. Multiple machine learning methods and models are applied to assess the wind turbine performance. This methodology has been applied in a case study with SCADA data of eight wind turbines. An analyst could benefit from the implementation of the methodology and the easy-to-interpret results shown in the proposed control chart, especially in cases in which there is less know-how about which variables have higher impact on systems performance.en
dc.language.isoengen
dc.publisherElsevieren
dc.rights@ Los autoresen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectMaintenance Managementen
dc.subjectwind energyen
dc.subjectMachine learningen
dc.titleA methodology for performance assessment at system level—Identification of operating regimes and anomaly detection in wind turbinesen
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2en
dcterms.sourceRenewable Energyen
local.contributor.groupTransformación de modelos de negocioes
local.description.peerreviewedtrueen
local.description.publicationfirstpage281en
local.description.publicationlastpage292en
local.identifier.doihttps://doi.org/10.1016/j.renene.2023.01.035en
local.contributor.otherinstitutionhttps://ror.org/03hp1m080es
local.source.detailsVolume 205, March 2023en
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501en


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Erregistro soila

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