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https://doi.org/10.5194/wes-2018-33
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Research articles 26 Apr 2018

Research articles | 26 Apr 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Wind Energy Science (WES).

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

Bart Doekemeijer1, Sjoerd Boersma1, Lucy Pao2, Torben Knudsen3, and Jan-Willem van Wingerden1 Bart Doekemeijer et al.
  • 1Delft Center for Systems and Control, Delft University of Technology, the Netherlands
  • 2Electrical, Computer & Energy Engineering, University of Colorado Boulder, Colorado, USA
  • 3Department of Electronic Systems, Aalborg University, 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 flow dynamics at a range of spatial and temporal scales relevant for control. A closed-loop control framework is proposed in which a simplified dynamical LES model is calibrated and used for optimization in real time. This paper presents an estimation solution with an Ensemble Kalman filter (EnKF) 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. Using exclusively turbine SCADA measurements, the adaptability to modeling errors and changes in atmospheric conditions (TI, wind speed) is shown. Convergence is reached within 400 seconds 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 two orders of magnitude faster. Using the calibration solution presented, the surrogate model can be used for accurate forecasting and optimization.

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Short summary
Most wind farm control algorithms in the literature rely on a simplified mathematical model, which requires constant calibration to the current conditions. This paper provides such an estimation algorithm for a dynamic farm model. Performance was demonstrated in high-fidelity simulations for a 9-turbine farm, accurately estimating the ambient conditions and wind field inside the farm at a low computational cost. This calibrated model can now be used for accurate, real-time wind farm control.
Most wind farm control algorithms in the literature rely on a simplified mathematical model,...
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