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.

Original languageEnglish
Title of host publicationProceedings of the 3rd IEEE Conference on Control Technology and Applications (CCTA 2019)
Place of PublicationPiscataway, NJ, USA
ISBN (Electronic)978-1-7281-2767-5
Publication statusPublished - 2019
Event3rd IEEE Conference on Control Technology and Applications, CCTA 2019 - Hong Kong, China
Duration: 19 Aug 201921 Aug 2019


Conference3rd IEEE Conference on Control Technology and Applications, CCTA 2019
CityHong Kong

ID: 68830242