Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control

Bart Doekemeijer, Sjoerd Boersma, Lucy Y. Pao, Torben Knudsen, Jan-Willem van Wingerden

Research output: Contribution to journalArticleScientificpeer-review

12 Citations (Scopus)
26 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)749-765
JournalWind Energy Science
Volume3
Issue number2
DOIs
Publication statusPublished - 2018

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  • Closing the loop in model-based wind farm control

    Doekemeijer, B. M., 2020, 147 p.

    Research output: ThesisDissertation (TU Delft)

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