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dc.contributorVan Wunnik, Lucas Philippe
dc.contributor.authorTarragó Aymerich, Martí
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Organització d'Empreses
dc.date.accessioned2019-04-10T10:12:06Z
dc.date.available2019-04-10T10:12:06Z
dc.date.issued2019-04-08
dc.identifier.urihttp://hdl.handle.net/2117/131580
dc.description.abstractThe transition from non-renewable to renewable energy production requires a detailed optimization and quantification of the generated power. The loss of power due to wake effect is a common problem for wind farms. The wake effect is the reduction of velocity and increase of turbulence in the wind flow downstream from a wind turbine. The wake effect is a complex multivariable phenomenon and its understanding iscapital forappropriate estimations of the power of a wind field and its turbines.This thesis builds an artificial neural network based on machine learning to model the performance of a single wind farm owned by WEICAN (Canada) taking into account the wake losses. Four different models have been considered. The first is not accounting for the wake losses; the second considers only the wake of the closest turbines; the third takes into account the wake in all the turbines; and the fourth provides all the data to the program in order to see what it can doon its own. The performance is evaluated using the mean absolute error, the root mean squared error and the normalized root mean square error.The best results areobtained using the third model, hence showing that the wake loss is significant and must be considered in the model. It is proved that with the appropriate input variables, an artificial neural network can predict the power of a wind farm accounting for the wake losses. The best performance of the artificial neural network is obtained for wind speeds up to 14 m/s
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Energies
dc.subject.lcshNeural networks (Computer science)
dc.subject.lcshRenewable energy sources
dc.subject.lcshWind turbines
dc.titleIntelligent estimation of the wake losses in wind farms
dc.typeMaster thesis
dc.subject.lemacXarxes neuronals (Informàtica)
dc.subject.lemacEnergies renovables
dc.subject.lemacAerogeneradors
dc.identifier.slugETSEIB-240.137863
dc.rights.accessOpen Access
dc.date.updated2019-04-08T05:21:58Z
dc.audience.educationlevelMàster
dc.audience.mediatorEscola Tècnica Superior d'Enginyeria Industrial de Barcelona
dc.contributor.covenanteeUniversitetet i Agder


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