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
Thomas Duc
CORRESPONDING AUTHOR
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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Cited
25 citations as recorded by crossref.
- Gaussian processes modifier adaptation with uncertain inputs for distributed learning and optimization of wind farms L. Andersson et al. 10.1016/j.ifacol.2020.12.1833
- Modelling cluster wakes and wind farm blockage N. Nygaard et al. 10.1088/1742-6596/1618/6/062072
- A new wake‐merging method for wind‐farm power prediction in the presence of heterogeneous background velocity fields L. Lanzilao & J. Meyers 10.1002/we.2669
- LiDAR and SCADA data processing for interacting wind turbine wakes with comparison to analytical wake models A. Hegazy et al. 10.1016/j.renene.2021.09.019
- Stochastic gradient descent for wind farm optimization J. Quick et al. 10.5194/wes-8-1235-2023
- A Meandering-Capturing Wake Model Coupled to Rotor-Based Flow-Sensing for Operational Wind Farm Flow Prediction M. Lejeune et al. 10.3389/fenrg.2022.884068
- Launch of the FarmConners Wind Farm Control benchmark for code comparison T. Göçmen et al. 10.1088/1742-6596/1618/2/022040
- Wind farm control ‐ Part I: A review on control system concepts and structures L. Andersson et al. 10.1049/rpg2.12160
- Analytical solutions for yawed wind-turbine wakes with application to wind-farm power optimization by active yaw control Z. Zhang et al. 10.1016/j.oceaneng.2024.117691
- A New Study on the Effect of the Partial Wake Generated in a Wind Farm S. Zergane et al. 10.3390/en17061498
- Experimental analysis of time delays in wind turbine wake interactions S. Macrì et al. 10.1088/1742-6596/1618/6/062058
- FarmConners wind farm flow control benchmark – Part 1: Blind test results T. Göçmen et al. 10.5194/wes-7-1791-2022
- A quantitative review of wind farm control with the objective of wind farm power maximization A. Kheirabadi & R. Nagamune 10.1016/j.jweia.2019.06.015
- Stochastic Dynamical Modeling of Wind Farm Turbulence A. Bhatt et al. 10.3390/en16196908
- Optimal tuning of engineering wake models through lidar measurements L. Zhan et al. 10.5194/wes-5-1601-2020
- Wake impact of constructing a new offshore wind farm zone on an existing downwind cluster: a case study of the Belgian Princess Elisabeth zone using FLORIS W. Munters et al. 10.1088/1742-6596/2265/2/022049
- Evaluation of the potential for wake steering for U.S. land-based wind power plants D. Bensason et al. 10.1063/5.0039325
- Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance E. Simley et al. 10.5194/wes-6-1427-2021
- Wake effect parameter calibration with large-scale field operational data using stochastic optimization P. Jain et al. 10.1016/j.apenergy.2023.121426
- Wind farm flow control: prospects and challenges J. Meyers et al. 10.5194/wes-7-2271-2022
- Single and multi-row turbine performance in bounded shear flow M. Juniper & T. Nishino 10.1017/jfm.2023.49
- Sensitivity of Wake Modelling Setups L. Kemme et al. 10.1088/1742-6596/2265/2/022007
- Comparison of modular analytical wake models to the Lillgrund wind plant N. Hamilton et al. 10.1063/5.0018695
- Verification of Prediction Method Based on Machine Learning under Wake Effect Using Real-Time Digital Simulator R. Park et al. 10.3390/en15249475
- A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes M. Amiri et al. 10.1016/j.rser.2024.114279
25 citations as recorded by crossref.
- Gaussian processes modifier adaptation with uncertain inputs for distributed learning and optimization of wind farms L. Andersson et al. 10.1016/j.ifacol.2020.12.1833
- Modelling cluster wakes and wind farm blockage N. Nygaard et al. 10.1088/1742-6596/1618/6/062072
- A new wake‐merging method for wind‐farm power prediction in the presence of heterogeneous background velocity fields L. Lanzilao & J. Meyers 10.1002/we.2669
- LiDAR and SCADA data processing for interacting wind turbine wakes with comparison to analytical wake models A. Hegazy et al. 10.1016/j.renene.2021.09.019
- Stochastic gradient descent for wind farm optimization J. Quick et al. 10.5194/wes-8-1235-2023
- A Meandering-Capturing Wake Model Coupled to Rotor-Based Flow-Sensing for Operational Wind Farm Flow Prediction M. Lejeune et al. 10.3389/fenrg.2022.884068
- Launch of the FarmConners Wind Farm Control benchmark for code comparison T. Göçmen et al. 10.1088/1742-6596/1618/2/022040
- Wind farm control ‐ Part I: A review on control system concepts and structures L. Andersson et al. 10.1049/rpg2.12160
- Analytical solutions for yawed wind-turbine wakes with application to wind-farm power optimization by active yaw control Z. Zhang et al. 10.1016/j.oceaneng.2024.117691
- A New Study on the Effect of the Partial Wake Generated in a Wind Farm S. Zergane et al. 10.3390/en17061498
- Experimental analysis of time delays in wind turbine wake interactions S. Macrì et al. 10.1088/1742-6596/1618/6/062058
- FarmConners wind farm flow control benchmark – Part 1: Blind test results T. Göçmen et al. 10.5194/wes-7-1791-2022
- A quantitative review of wind farm control with the objective of wind farm power maximization A. Kheirabadi & R. Nagamune 10.1016/j.jweia.2019.06.015
- Stochastic Dynamical Modeling of Wind Farm Turbulence A. Bhatt et al. 10.3390/en16196908
- Optimal tuning of engineering wake models through lidar measurements L. Zhan et al. 10.5194/wes-5-1601-2020
- Wake impact of constructing a new offshore wind farm zone on an existing downwind cluster: a case study of the Belgian Princess Elisabeth zone using FLORIS W. Munters et al. 10.1088/1742-6596/2265/2/022049
- Evaluation of the potential for wake steering for U.S. land-based wind power plants D. Bensason et al. 10.1063/5.0039325
- Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance E. Simley et al. 10.5194/wes-6-1427-2021
- Wake effect parameter calibration with large-scale field operational data using stochastic optimization P. Jain et al. 10.1016/j.apenergy.2023.121426
- Wind farm flow control: prospects and challenges J. Meyers et al. 10.5194/wes-7-2271-2022
- Single and multi-row turbine performance in bounded shear flow M. Juniper & T. Nishino 10.1017/jfm.2023.49
- Sensitivity of Wake Modelling Setups L. Kemme et al. 10.1088/1742-6596/2265/2/022007
- Comparison of modular analytical wake models to the Lillgrund wind plant N. Hamilton et al. 10.1063/5.0018695
- Verification of Prediction Method Based on Machine Learning under Wake Effect Using Real-Time Digital Simulator R. Park et al. 10.3390/en15249475
- A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes M. Amiri et al. 10.1016/j.rser.2024.114279
Latest update: 29 Jun 2024
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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