Sea trial results of a predictive algorithm at the Mutriku Wave power plant and controllers assessment based on a detailed plant model

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dc.contributor.authorFaÿ, François-Xavier
dc.contributor.authorRobles, Eider
dc.contributor.authorMarcos, Marga
dc.contributor.authorAldaiturriaga, Endika
dc.contributor.authorCamacho, Eduardo F.
dc.contributor.institutionTecnalia Research & Innovation
dc.contributor.institutionRENOVABLES EFICIENCIA ENERGETICA Y CIRCULARIDAD
dc.contributor.institutionRENOVABLES OFFSHORE
dc.date.issued2020-02
dc.descriptionPublisher Copyright: © 2019 Elsevier Ltd
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.en
dc.description.statusPeer reviewed
dc.format.extent21
dc.format.extent5252926
dc.identifier.citationFaÿ , F-X , Robles , E , Marcos , M , Aldaiturriaga , E & Camacho , E F 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 , vol. 146 , pp. 1725-1745 . https://doi.org/10.1016/j.renene.2019.07.129
dc.identifier.doi10.1016/j.renene.2019.07.129
dc.identifier.issn0960-1481
dc.identifier.otherresearchoutputwizard: 11556/784
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85073705696&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofRenewable Energy
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsWave energy
dc.subject.keywordsMutriku
dc.subject.keywordsReal sea testing
dc.subject.keywordsPredictive control strategies
dc.subject.keywordsPower take-off
dc.subject.keywordsBiradial turbine
dc.subject.keywordsOPERA H2020
dc.subject.keywordsWave energy
dc.subject.keywordsMutriku
dc.subject.keywordsReal sea testing
dc.subject.keywordsPredictive control strategies
dc.subject.keywordsPower take-off
dc.subject.keywordsBiradial turbine
dc.subject.keywordsOPERA H2020
dc.subject.keywordsRenewable Energy, Sustainability and the Environment
dc.subject.keywordsSDG 7 - Affordable and Clean Energy
dc.subject.keywordsProject ID
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/H2020/654444/EU/Open Sea Operating Experience to Reduce Wave Energy Cost/OPERA
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/H2020/654444/EU/Open Sea Operating Experience to Reduce Wave Energy Cost/OPERA
dc.subject.keywordsFunding Info
dc.subject.keywordsThe work was funded by European Union's Horizon 2020 research and innovation program, OPERA Project under grantagreement No 654444, and the Basque Government under project IT1324-19. We acknowledge Ente Vasco de la Energía (EVE) for theaccess of the Mutriku plant and Oceantec in their support during the sea trials. The authors thank Joannes Berques (Tecnalia) for hiscontribution on the wave climate analysis at Mutriku and Borja de Miguel (IDOM) for his insights on the hydrodynamics modelling. Special thanks go to Temoana Menard in the study of the polytropic air model during its internship at Tecnalia.
dc.subject.keywordsThe work was funded by European Union's Horizon 2020 research and innovation program, OPERA Project under grantagreement No 654444, and the Basque Government under project IT1324-19. We acknowledge Ente Vasco de la Energía (EVE) for theaccess of the Mutriku plant and Oceantec in their support during the sea trials. The authors thank Joannes Berques (Tecnalia) for hiscontribution on the wave climate analysis at Mutriku and Borja de Miguel (IDOM) for his insights on the hydrodynamics modelling. Special thanks go to Temoana Menard in the study of the polytropic air model during its internship at Tecnalia.
dc.titleSea trial results of a predictive algorithm at the Mutriku Wave power plant and controllers assessment based on a detailed plant modelen
dc.typejournal article
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