@article{792b026fe9b44fada31d716be951ff20,
title = "Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control",
abstract = "Wind farm control often relies on computationally inexpensive surrogate models to predict the dynamics inside a farm. However, the reliability of these models over the spectrum of wind farm operation remains questionable due to the many uncertainties in the atmospheric conditions and tough-to-model dynamics at a range of spatial and temporal scales relevant for control. A closed-loop control framework is proposed in which a simplified model is calibrated and used for optimization in real time. This paper presents a joint state-parameter estimation solution with an ensemble Kalman filter at its core, which calibrates the surrogate model to the actual atmospheric conditions. The estimator is tested in high-fidelity simulations of a nine-turbine wind farm. Exclusively using measurements of each turbine's generated power, the adaptability to modeling errors and mismatches in atmospheric conditions is shown. Convergence is reached within 400 s of operation, after which the estimation error in flow fields is negligible. At a low computational cost of 1.2 s on an 8-core CPU, this algorithm shows comparable accuracy to the state of the art from the literature while being approximately 2 orders of magnitude faster.",
author = "Bart Doekemeijer and Sjoerd Boersma and Pao, {Lucy Y.} and Torben Knudsen and {van Wingerden}, Jan-Willem",
year = "2018",
doi = "10.5194/wes-3-749-2018",
language = "English",
volume = "3",
pages = "749--765",
journal = "Wind Energy Science",
issn = "2366-7443",
publisher = "Copernicus",
number = "2",
}