Publication:
Wind turbine power coefficient models based on neural networks and polynomial fitting

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dc.affiliation.dptoUC3M. Departamento de Ingeniería Eléctricaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Redes y Sistemas de Energía Eléctrica (REDES)es
dc.contributor.authorCarpintero Rentería, Miguel
dc.contributor.authorSantos Martín, David
dc.contributor.authorLent, Andrew
dc.contributor.authorRamos González, Carlos
dc.date.accessioned2022-05-03T12:40:17Z
dc.date.available2022-05-03T12:40:17Z
dc.date.issued2020-08-01
dc.description.abstractThe power coefficient parameter represents the aerodynamic wind turbine efficiency. Since the 1980s, several equations have been used in the literature to study the power coefficient as a function of the tip speed ratio and the pitch angle. In this study, these equations are reviewed and compared. A corrected blade element momentum algorithm is used to generate three sets of data representing different ranges of wind turbines, going from 2 to 10 MW. With this information, two power coefficient models are proposed and shared. One model is based on a polynomial fitting, whereas the other is based on neural network techniques. Both were trained with the blade element momentum model output data and showed good behaviour for all operating ranges. In the results, compared to all the algorithms found in the literature, the proposed models reduced the power coefficient error by at least 55% compared to the best numerical approximation from the literature. An error reduction in the power coefficient parameter may have a large impact on many wind energy conversion system studies, such as those treating dynamic and transient behavioursen
dc.description.statusPublicadoes
dc.format.extent8
dc.identifier.bibliographicCitationIET Renewable Power Generation, (2020), 14(11), pp.: 1841-1849.en
dc.identifier.doihttps://doi.org/10.1049/iet-rpg.2019.1162
dc.identifier.issn1752-1416
dc.identifier.publicationfirstpage1841
dc.identifier.publicationissue11
dc.identifier.publicationlastpage1849
dc.identifier.publicationtitleIET Renewable Power Generationen
dc.identifier.publicationvolume14
dc.identifier.urihttps://hdl.handle.net/10016/34672
dc.identifier.uxxiAR/0000027421
dc.language.isoengen
dc.publisherInstitution of Engineering and Technologyen
dc.rights© The Institution of Engineering and Technology 2020en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaIngeniería Industriales
dc.subject.otherBladesen
dc.subject.otherAerodynamicsen
dc.subject.otherWind turbinesen
dc.subject.otherPolynomial approximationen
dc.subject.otherNeural netsen
dc.subject.otherCurve fittingen
dc.subject.otherMechanical engineering computingen
dc.titleWind turbine power coefficient models based on neural networks and polynomial fittingen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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