Print Email Facebook Twitter Joint state-parameter estimation for a control-oriented LES wind farm model Title Joint state-parameter estimation for a control-oriented LES wind farm model Author Doekemeijer, B.M. (TU Delft Team Jan-Willem van Wingerden) Boersma, S. (TU Delft Team Jan-Willem van Wingerden) Pao, L. Y. (University of Colorado) van Wingerden, J.W. (TU Delft Team Jan-Willem van Wingerden) Date 2018 Abstract Wind farm control research typically relies on computationally inexpensive, surrogate models for real-time optimization. However, due to the large time delays involved, changing atmospheric conditions and tough-to-model flow and turbine dynamics, these surrogate models need constant calibration. In this paper, a novel real-time (joint state-parameter) estimation solution for a medium-fidelity dynamical wind farm model is presented. In this work, we demonstrate the estimation of the freestream wind speed, local turbulence, and local wind field in a two-turbine wind farm using exclusively turbine power measurements. The estimator employs an Ensemble Kalman filter with a low computational cost of approximately 1.0 s per timestep on a dual-core notebook CPU. This work presents an essential building block for real-time wind farm control using computationally efficient dynamical wind farm models. To reference this document use: http://resolver.tudelft.nl/uuid:92d31d6c-09ab-47ac-bc18-4375af8393de DOI https://doi.org/10.1088/1742-6596/1037/3/032013 Publisher IOP Publishing, Bristol, UK Source Journal of Physics: Conference Series: The Science of Making Torque from Wind (TORQUE 2018), 1037 Event TORQUE 2018, 2018-06-20 → 2018-06-22, Milano, Italy Series Journal of Physics: Conference Series, 1742-6588 Part of collection Institutional Repository Document type conference paper Rights © 2018 B.M. Doekemeijer, S. Boersma, L. Y. Pao, J.W. van Wingerden Files PDF pdf.pdf 1.61 MB Close viewer /islandora/object/uuid:92d31d6c-09ab-47ac-bc18-4375af8393de/datastream/OBJ/view