Print Email Facebook Twitter Data-Driven LIDAR Feedforward Predictive Wind Turbine Control Title Data-Driven LIDAR Feedforward Predictive Wind Turbine Control Author Dinkla, R.T.O. (TU Delft Team Jan-Willem van Wingerden) Oomen, T.A.E. (TU Delft Team Jan-Willem van Wingerden; Eindhoven University of Technology) van Wingerden, J.W. (TU Delft Team Jan-Willem van Wingerden) Mulders, S.P. (TU Delft Team Mulders) Date 2023 Abstract Light Detection and Ranging (LIDAR)-assisted Model Predictive Control (MPC) for wind turbine control has received much attention for its ability to incorporate future wind speed disturbance information in a receding horizon optimal control problem. However, the growth of wind turbine sizes results in increasing system complexity and system interactions, and complicates the design of model-based controllers like MPC. Together with increasing data availability, this obstacle motivates the use of direct data-driven predictive control approaches like Subspace Predictive Control (SPC). An SPC implementation is developed that both does not suffer from traditional, potentially detrimental closed-loop identification bias and incorporates past and future (not necessarily periodic) disturbance information. Simulations of the presented method for above-rated wind turbine rotor speed regulation using pitch control demonstrate the capabilities of the data-driven SPC algorithm for increasing degrees of wind speed disturbance information in the developed framework. To reference this document use: http://resolver.tudelft.nl/uuid:437fafea-5372-48d8-8519-1387f876fafb DOI https://doi.org/10.1109/CCTA54093.2023.10252439 Publisher IEEE Embargo date 2024-03-22 ISBN 979-8-3503-3544-6 Source Proceedings of the 2023 IEEE Conference on Control Technology and Applications, CCTA 2023 Event 2023 IEEE Conference on Control Technology and Applications, CCTA 2023, 2023-08-16 → 2023-08-18, Bridgetown, Barbados 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 © 2023 R.T.O. Dinkla, T.A.E. Oomen, J.W. van Wingerden, S.P. Mulders Files PDF Data_Driven_LIDAR_Feedfor ... ontrol.pdf 1.89 MB Close viewer /islandora/object/uuid:437fafea-5372-48d8-8519-1387f876fafb/datastream/OBJ/view