Articles | Volume 5, issue 1
https://doi.org/10.5194/wes-5-427-2020
© Author(s) 2020. 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-5-427-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Mar 2020
Research article |
| 31 Mar 2020
| 31 Mar 2020
Analytical model for the power–yaw sensitivity of wind turbines operating in full wake
Department of Wind Energy, Technical University of Denmark (DTU), Frederiksborgvej 399, 4000 Roskilde, Denmark
Albert M. Urbán
Department of Wind Energy, Technical University of Denmark (DTU), Frederiksborgvej 399, 4000 Roskilde, Denmark
Søren Juhl Andersen
Department of Wind Energy, Technical University of Denmark (DTU), Anker Engelunds Vej 1, 2800 Lyngby, Denmark
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36 citations as recorded by crossref.
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35 citations as recorded by crossref.
- The revised FLORIDyn model: implementation of heterogeneous flow and the Gaussian wake M. Becker et al. 10.5194/wes-7-2163-2022
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- Analytical model for the power production of a yaw-misaligned wind turbine J. Lu et al. 10.1063/5.0174267
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- Optimal closed-loop wake steering – Part 1: Conventionally neutral atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-5-1315-2020
- Large-eddy simulation on the similarity between wakes of wind turbines with different yaw angles Z. Li & X. Yang 10.1017/jfm.2021.495
- FarmConners wind farm flow control benchmark – Part 1: Blind test results T. Göçmen et al. 10.5194/wes-7-1791-2022
- Investigation of far-wake models coupled with yaw-induction control for power optimization K. Heck et al. 10.1088/1742-6596/2767/9/092103
- Wake redirection control for offshore wind farm power and fatigue multi-objective optimisation based on a wind turbine load indicator J. Sun et al. 10.1016/j.energy.2024.133893
- Turbine power loss during yaw-misaligned free field tests at different atmospheric conditions P. Hulsman et al. 10.1088/1742-6596/2265/3/032074
- A Data-Mining Compensation Approach for Yaw Misalignment on Wind Turbine Y. Bao & Q. Yang 10.1109/TII.2021.3065702
- Optimal closed-loop wake steering – Part 2: Diurnal cycle atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-7-345-2022
- On the power and control of a misaligned rotor – beyond the cosine law S. Tamaro et al. 10.5194/wes-9-1547-2024
- 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
- Enhanced Modeling of Joint Yaw and Axial Induction Control Using Blade Element Momentum Methods J. Liew et al. 10.1088/1742-6596/2767/3/032018
- Modeling the effect of wind speed and direction shear on utility‐scale wind turbine power production S. Mata et al. 10.1002/we.2917
- Providing power reserve for secondary grid frequency regulation of offshore wind farms through yaw control Y. Oudich et al. 10.1002/we.2845
- Wind plant power maximization via extremum seeking yaw control: A wind tunnel experiment D. Kumar et al. 10.1002/we.2799
- Unified momentum model for rotor aerodynamics across operating regimes J. Liew et al. 10.1038/s41467-024-50756-5
- Wake Effect Quantification using SCADA Data and LES Modelling of an Operational Offshore Wind Farm W. Chanprasert et al. 10.1088/1742-6596/2767/9/092012
- Validation of induction/steering reserve-boosting active power control by a wind tunnel experiment with dynamic wind direction changes S. Tamaro et al. 10.1088/1742-6596/2767/9/092067
- Influence of atmospheric conditions on the power production of utility-scale wind turbines in yaw misalignment M. Howland et al. 10.1063/5.0023746
- A data-driven reduced-order model for rotor optimization N. Peters et al. 10.5194/wes-8-1201-2023
- Development and validation of a hybrid data-driven model-based wake steering controller and its application at a utility-scale wind plant P. Bachant et al. 10.5194/wes-9-2235-2024
- Layout and yaw optimisation of an offshore wind farm through analytical modelling D. Sukhman et al. 10.1088/1742-6596/2626/1/012058
- Aerodynamic characterization of two tandem wind turbines under yaw misalignment control using actuator line model Y. Tu et al. 10.1016/j.oceaneng.2023.114992
- Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm M. van Beek et al. 10.3390/en14051293
- Data-driven yaw misalignment correction for utility-scale wind turbines L. Gao & J. Hong 10.1063/5.0056671
- Model predictive control of wakes for wind farm power tracking A. Sterle et al. 10.1088/1742-6596/2767/3/032005
- Dynamic wind farm flow control using free-vortex wake models M. van den Broek et al. 10.5194/wes-9-721-2024
- A LiDAR-Based Active Yaw Control Strategy for Optimal Wake Steering in Paired Wind Turbines E. Mahmoodi et al. 10.3390/en17225635
- Wind turbine wake control strategies: A review and concept proposal R. Nash et al. 10.1016/j.enconman.2021.114581
- Assessing Closed-Loop Data-Driven Wind Farm Control Strategies within a Wind Tunnel P. Hulsman et al. 10.1088/1742-6596/2767/3/032049
Latest update: 13 Dec 2024
Short summary
In wind farms, the interaction between neighboring turbines can cause notable power losses. The focus of the paper is on how the combination of turbine yaw misalignment and wake effects influences the power loss in a wind turbine. The results of the paper show a more notable power loss due to turbine misalignment when turbines are closely spaced. The presented conclusions enable better predictions of a turbine's power production, which can assist the wind farm design process.
In wind farms, the interaction between neighboring turbines can cause notable power losses. The...
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