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Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
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https://doi.org/10.5194/wes-2019-46
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-2019-46
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 22 Aug 2019

Submitted as: research article | 22 Aug 2019

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

Optimizing Wind Farm Control through Wake Steering using Surrogate Models based on High Fidelity Simulations

Paul Hulsman1, Søren Juhl Andersen2, and Tuhfe Göçmen3 Paul Hulsman et al.
  • 1ForWind – University of Oldenburg, Research Group Wind Energy Systems, Küpkersweg 70, 26129 Oldenburg, Germany
  • 2Wind Energy Department, Technical University of Denmark, Anker Engelunds Vej 1, DK-2800 Lyngby, Denmark
  • 3Wind Energy Department, Technical University of Denmark, Frederiksborgvej 399, DK-4000 Roskilde, Denmark

Abstract. This paper aims to develop fast and reliable surrogate models for yaw-based wind farm control. The surrogates, based on polynomial chaos expansion (PCE), are built using high fidelity flow simulations combined with aeroelastic simulations of the turbine performance and loads. Developing a model for wind farm control is a challenging control problem due to the time-varying dynamics of the wake. Both the power output and the loading of the turbines are included in the optimization of wind farm control strategies. Optimization results performed using two Vestas V27 turbines in a row for a specific atmospheric condition suggest that a power gain of almost 3 % ± 1 % can be achieved at close spacing by yawing the upstream turbine more than 15°. At larger spacing, the power gain the optimization shows that yawing is not beneficial as the optimization reverts to normal operation. Furthermore, it was also identified that a reduction of the equivalent loads was obtained at the cost of power production. The total power gains are discussed in relation to the associated model errors and the uncertainty of the surrogate models used in the optimization, and the implication for wind farm control.

Paul Hulsman et al.
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Status: final response (author comments only)
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Paul Hulsman et al.
Paul Hulsman et al.
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This paper aims to develop fast and reliable surrogate models for yaw-based wind farm control. The surrogates, based on polynomial chaos expansion, are built using high fidelity flow simulations combined with aeroelastic simulations of the turbine performance and loads. Optimization results performed using two Vestas V27 turbines in a row for a specific atmospheric condition suggest that a power gain of almost 3 % ± 1 % can be achieved at close spacing by yawing the upstream turbine more than 15°.
This paper aims to develop fast and reliable surrogate models for yaw-based wind farm control....
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