Volume 3, issue 2 | Copyright
Wind Energ. Sci., 3, 749-765, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research articles 24 Oct 2018

Research articles | 24 Oct 2018

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

Bart M. Doekemeijer1, Sjoerd Boersma1, Lucy Y. Pao2, Torben Knudsen3, and Jan-Willem van Wingerden1 Bart M. Doekemeijer et al.
  • 1Delft Center for Systems and Control, Delft University of Technology, Delft, the Netherlands
  • 2Electrical, Computer & Energy Engineering, University of Colorado Boulder, Boulder, CO, USA
  • 3Department of Electronic Systems, Aalborg University, Aalborg, Denmark

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 400s of operation, after which the estimation error in flow fields is negligible. At a low computational cost of 1.2s 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.

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Short summary
Most wind farm control algorithms in the literature rely on a simplified mathematical model that requires constant calibration to the current conditions. This paper provides such an estimation algorithm for a dynamic model capturing the turbine power production and flow field at hub height. Performance was demonstrated in high-fidelity simulations for two-turbine and nine-turbine farms, accurately estimating the ambient conditions and wind field inside the farms at a low computational cost.
Most wind farm control algorithms in the literature rely on a simplified mathematical model that...