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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2020-02
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Abstract
Improving 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.
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Publisher Copyright: © 2019 Elsevier Ltd
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Wave energy , Mutriku , Real sea testing , Predictive control strategies , Power take-off , Biradial turbine , OPERA H2020 , Wave energy , Mutriku , Real sea testing , Predictive control strategies , Power take-off , Biradial turbine , OPERA H2020 , Renewable Energy, Sustainability and the Environment , SDG 7 - Affordable and Clean Energy , Project ID , info:eu-repo/grantAgreement/EC/H2020/654444/EU/Open Sea Operating Experience to Reduce Wave Energy Cost/OPERA , info:eu-repo/grantAgreement/EC/H2020/654444/EU/Open Sea Operating Experience to Reduce Wave Energy Cost/OPERA , Funding Info , The 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. , The 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.
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Faÿ , 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