Print Email Facebook Twitter Convex Model Predictive Control for Down-regulation Strategies in Wind Turbines Title Convex Model Predictive Control for Down-regulation Strategies in Wind Turbines Author Gonzalez Silva, J. (TU Delft Team Riccardo Ferrari) Ferrari, Riccardo M.G. (TU Delft Team Riccardo Ferrari) van Wingerden, J.W. (TU Delft Team Jan-Willem van Wingerden) Date 2022 Abstract Wind turbine (WT) controllers are often geared towards maximum power extraction, while suitable operating constraints should be guaranteed such that WT components are protected from failures. Control strategies can be also devised to reduce the generated power, for instance to track a power reference provided by the grid operator. They are called down-regulation strategies and allow to balance power generation and grid loads, as well as to provide ancillary grid services, such as frequency regulation. Although this balance is limited by the wind availability and grid demand, the quality of wind energy can be improved by introducing down-regulation strategies that make use of the kinetic energy of the turbine dynamics. This paper shows how the kinetic energy in the rotating components of turbines can be used as an additional degree-of-freedom by different down-regulation strategies. In particular we explore the power tracking problem based on convex model predictive control (MPC) at a single wind turbine. The use of MPC allows us to introduce a further constraint that guarantees flow stability and avoids stall conditions. Simulation results are used to illustrate the performance of the developed down-regulation strategies. Notably, by maximizing rotor speeds, and thus kinetic energy, the turbine can still temporarily guarantee tracking of a given power reference even when occasional saturation of the available wind power occurs. In the study case we proved that our approach can guarantee power tracking in saturated conditions for 10 times longer than with traditional down-regulation strategies. Subject Target trackingWind energyWind power generationWind farmsAerodynamicStability analysisWind turbines To reference this document use: http://resolver.tudelft.nl/uuid:554edeb2-cfa1-4cf7-913a-300b28fb6503 DOI https://doi.org/10.1109/CDC51059.2022.9993421 Publisher IEEE Embargo date 2023-07-10 ISBN 978-1-6654-6761-2 Source Proceedings of the IEEE 61st Conference on Decision and Control (CDC 2022) Event IEEE 61st Conference on Decision and Control (CDC 2022), 2022-12-06 → 2022-12-09, Cancún, Mexico Bibliographical note Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type conference paper Rights © 2022 J. Gonzalez Silva, Riccardo M.G. Ferrari, J.W. van Wingerden Files PDF Convex_Model_Predictive_C ... rbines.pdf 1.23 MB Close viewer /islandora/object/uuid:554edeb2-cfa1-4cf7-913a-300b28fb6503/datastream/OBJ/view