Articles | Volume 4, issue 2
https://doi.org/10.5194/wes-4-287-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-4-287-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 May 2019
Research article |
| 22 May 2019
| 22 May 2019
Local turbulence parameterization improves the Jensen wake model and its implementation for power optimization of an operating wind farm
ENGIE Green France, 59 rue Denuzière, 69002 Lyon, France
Olivier Coupiac
ENGIE Green France, 59 rue Denuzière, 69002 Lyon, France
Nicolas Girard
ENGIE Green France, 59 rue Denuzière, 69002 Lyon, France
Gregor Giebel
DTU Wind Energy, Risø Campus, Frederiksborgvej 399, 4000 Roskilde, Denmark
Tuhfe Göçmen
DTU Wind Energy, Risø Campus, Frederiksborgvej 399, 4000 Roskilde, Denmark
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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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