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Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
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Discussion papers
https://doi.org/10.5194/wes-2018-62
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-2018-62
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research articles 11 Oct 2018

Research articles | 11 Oct 2018

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

Local turbulence parameterization improves the Jensen wake model and its implementation for power optimization of an operating wind farm

Thomas Duc1, Olivier Coupiac1, Nicolas Girard1, Gregor Giebel2, and Tuhfe Göçmen2 Thomas Duc et al.
  • 1ENGIE Green France, 59 rue Denuzière, 69002 Lyon, France
  • 2DTU Wind Energy, Risø Campus, Frederiksborgvej 399, 4000 Roskilde, Denmark

Abstract. In this paper, a new calculation procedure to improve the accuracy of the Jensen wake model for operating wind farms is proposed. In this procedure the wake decay constant is updated locally at each wind turbine based on the turbulence intensity measurement provided by the nacelle anemometer. This procedure was tested against experimental data at onshore wind farm La Sole du Moulin Vieux (SMV) in France and the offshore wind farm Horns Rev-I in Denmark. Results indicate that the wake deficit at each wind turbine is described more accurately than when using the original model, reducing the error from 15–20% to approximately 5%. Furthermore, this new model properly calibrated for the SMV wind farm is then used for coordinated control purposes. Assuming an axial induction control strategy, and following a model predictive approach, new power settings leading to an increased overall power production of the farm are derived. Power gains found are in the order of 2.5% for a two wind turbine case with close spacing and 1 to 1.5% for a row of five wind turbines with a larger spacing. Finally, the uncertainty of the updated Jensen model is quantified considering the model inputs. When checked against the predicted power gain, the uncertainty of the model estimations is seen to be excessive, reaching approximately 4%, which indicates the difficulty of field observations for such a gain. Nevertheless, the optimized settings are to be implemented during a field test campaign at SMV wind farm in scope of the national project SMARTEOLE.

Thomas Duc et al.
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Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Thomas Duc et al.
Thomas Duc et al.
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Latest update: 14 Dec 2018
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Short summary
Wind turbine wake recovery is very sensitive to ambient atmospheric conditions. This paper presents a way of including a local turbulence intensity estimation from SCADA into the Jensen wake model to improve its accuracy. This new model procedure is used to optimize power production of an operating wind farm, and shows that some gains can be expected even if uncertainties remain high. These optimized settings are to be implemented in a field test campaign in the scope of the SMARTEOLE project.
Wind turbine wake recovery is very sensitive to ambient atmospheric conditions. This paper...
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