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dc.creatorFaÿ, François-Xavieres
dc.creatorRobles, Eideres
dc.creatorMarcos, Margaes
dc.creatorAldaiturriaga, Endikaes
dc.creatorCamacho, Eduardo F.es
dc.date.accessioned2021-05-04T15:18:26Z
dc.date.available2021-05-04T15:18:26Z
dc.date.issued2020
dc.identifier.citationFaÿ, F., Robles, E., Marcos, M., Aldaiturriaga, E. y Fernández Camacho, E. (2020). Sea trial results of a predictive algorithm at the Mutriku Wave power plant and controllers assessment based on a detailed plant model. Renewable energy, 146, 1725-1745.
dc.identifier.issn0960-1481es
dc.identifier.urihttps://hdl.handle.net/11441/108457
dc.description.abstractImproving the power production in wave energy plants is essential to lower the cost of energy production from this type of installations. Oscillating Water Column is among the most studied technologies to convert the wave energy into a useful electrical one. In this paper, three control algorithms are developed to control the biradial turbine installed in the Mutriku Wave Power Plant. The work presents a comparison of their main advantages and drawbacks first from numerical simulation results and then with practical implementation in the real plant, analysing both performance and power integration into the grid. The wave-to-wire model used to develop and assess the controllers is based on linear wave theory and adjusted with operational data measured at the plant. Three different controllers which use the generator torque as manipulated variable are considered. Two of them are adaptive controllers and the other one is a nonlinear Model Predictive Control (MPC) algorithm which uses information about the future waves to compute the control actions. The best adaptive controller and the predictive one are then tested experimentally in the real power plant of Mutriku, and the performance analysis is completed with operational results. A real time sensor installed in front of the plant gives information on the incoming waves used by the predictive algorithm. Operational data are collected during a two-week testing period, enabling a thorough comparison. An overall increase over 30% in the electrical power production is obtained with the predictive control law in comparison with the reference adaptive controller.es
dc.description.sponsorshipUnión Europea 654444es
dc.description.sponsorshipGobierno Vasco IT1324-19es
dc.formatapplication/pdfes
dc.format.extent21 p.es
dc.language.isoenges
dc.publisherElsevieres
dc.relation.ispartofRenewable energy, 146, 1725-1745.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectWave energyes
dc.subjectMutrikues
dc.subjectReal sea testinges
dc.subjectPredictive control strategieses
dc.subjectPower take-offes
dc.subjectBiradial turbinees
dc.subjectOPERA H2020es
dc.titleSea trial results of a predictive algorithm at the Mutriku Wave power plant and controllers assessment based on a detailed plant modeles
dc.typeinfo:eu-repo/semantics/articlees
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Ingeniería de Sistemas y Automáticaes
dc.relation.projectID654444es
dc.relation.projectIDIT1324-19es
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0960148119311498es
dc.identifier.doi10.1016/j.renene.2019.07.129es
dc.journaltitleRenewable energyes
dc.publication.volumen146es
dc.publication.initialPage1725es
dc.publication.endPage1745es
dc.identifier.sisius21865926es
dc.contributor.funderHorizonte 2020es

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