Model-based closed-loop wind farm control for power maximization using Bayesian optimization

Model-based closed-loop wind farm control for power maximization using Bayesian optimization: A large eddy simulation study

Doekemeijer, B.M. (TU Delft Team Jan-Willem van Wingerden)
van der Hoek, D.C. (TU Delft Team Jan-Willem van Wingerden)
van Wingerden, J.W. (TU Delft Team Jan-Willem van Wingerden)

2019

Modern wind farm control (WFC) methods in the literature typically rely on a surrogate model of the farm dynamics that is computationally inexpensive to enable real-time computations. As it is very difficult to model all the relevant wind farm dynamics accurately, a closed-loop approach is a prerequisite for reliable WFC. As one of the few in its field, this paper showcases a closed-loop wind farm control solution, which leverages a steady-state surrogate model and Bayesian optimization to maximize the wind-farm-wide power production. The estimated quantities are the time-averaged ambient wind direction, wind speed and turbulence intensity. This solution is evaluated for a wind farm with nine 10 MW wind turbines in large-eddy simulation, showing a time-averaged power gain of 4.4%. This is the first WFC algorithm that is tested for wind turbines of such scale in high fidelity.

http://resolver.tudelft.nl/uuid:583e9c99-1e4a-45b0-aa54-a23145cf823d

https://doi.org/10.1109/CCTA.2019.8920587

IEEE, Piscataway, NJ, USA

978-1-7281-2767-5

Proceedings of the 3rd IEEE Conference on Control Technology and Applications (CCTA 2019)

3rd IEEE Conference on Control Technology and Applications, CCTA 2019, 2019-08-19 → 2019-08-21, Hong Kong, China

Accepted Author Manuscript

Institutional Repository

conference paper

© 2019 B.M. Doekemeijer, D.C. van der Hoek, J.W. van Wingerden