Wind turbine modeling, maximum power point tracking (MPPT), and experimental validation

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dc.contributor.authorEben Zaid, Ichrak
dc.contributor.authorSamuel Raj, Daison Stallon
dc.contributor.authorVidal Seguí, Yolanda
dc.contributor.authorBoussada, Moez
dc.contributor.authorSaid Nouri, Ahmed
dc.contributor.groupUniversitat Politècnica de Catalunya. CoDAlab - Control, Dades i Intel·ligència Artificial
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Matemàtiques
dc.date.accessioned2024-10-07T11:56:41Z
dc.date.available2024-10-07T11:56:41Z
dc.date.issued2024-07-29
dc.description.abstractThe research presented is driven by the global increase in wind power capacity and the commitment of the scientific community to facilitate its integration into electrical grids. The focus of this study is the modeling of a wind turbine system, beginning with its mechanical components. To ensure the production of power at optimal levels, a control strategy for Maximum Power Point Tracking (MPPT) based on Optimal Torque (OT) has been adopted. The model and control method, developed in Matlab/Simulink, have demonstrated their precision and efficacy through experimental verification using SCADA data acquired from an operational wind turbine.
dc.description.peerreviewedPeer Reviewed
dc.description.sponsorshipThis work was supported by the Ministry of Higher Education and Scientific Research-Tunisia;by the Spanish Agencia Estatal de Investigaci\'on (AEI) -Ministerio de Econom\'ia, Industria y Competitividad (MINECO), and the Fondo Europeo de Desarrollo Regional (FEDER) through the research projects PID2021-122132OB-C21 and TED2021-129512B-I00; and by the Generalitat de Catalunya through the research project 2021-SGR-01044.
dc.description.versionPostprint (published version)
dc.format.extent6 p.
dc.identifier.citationEben, I. [et al.]. Wind turbine modeling, maximum power point tracking (MPPT), and experimental validation. "Renewable energy and power quality journal", 29 Juliol 2024, vol. 22, núm. 2, p. 1-6.
dc.identifier.doi10.52152/3923
dc.identifier.issn2172-038X
dc.identifier.urihttps://hdl.handle.net/2117/415420
dc.language.isoeng
dc.relation.projectidinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-122132OB-C21/ES/DESARROLLO Y VALIDACION DE ESTRATEGIAS DE APRENDIZAJE PROFUNDO Y AUTOMATICO PARA EL MANTENIMIENTO PREDICTIVO Y DETECCION TEMPRANA DE DAÑOS ESTRUCTURALES EN AEROGENERADORES/
dc.relation.projectidinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/TED2021-129512B-I00/ES/Gemelos digitales para la monitorización de la condición de aerogeneradores/
dc.relation.publisherversionhttps://repqj.com/index.php/repqj/article/view/3923
dc.rights.accessOpen Access
dc.subjectÀrees temàtiques de la UPC::Energies::Energia eòlica::Aerogeneradors
dc.subject.lcshWind turbines
dc.subject.lemacAerogeneradors
dc.subject.otherWind turbine modelling
dc.subject.otherMatlab/Simulink
dc.subject.otherMPPT
dc.subject.otherSCADA data
dc.subject.otherValidation
dc.titleWind turbine modeling, maximum power point tracking (MPPT), and experimental validation
dc.typeArticle
dspace.entity.typePublication
local.citation.authorEben, I.; Samuel, D.; Vidal, Y.; Boussada, M.; Said, A.
local.citation.endingPage6
local.citation.number2
local.citation.publicationNameRenewable energy and power quality journal
local.citation.startingPage1
local.citation.volume22
local.identifier.drac39754867

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