Articles | Volume 7, issue 1
https://doi.org/10.5194/wes-7-345-2022
© Author(s) 2022. 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-7-345-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Feb 2022
Research article |
| 09 Feb 2022
| 09 Feb 2022
Optimal closed-loop wake steering – Part 2: Diurnal cycle atmospheric boundary layer conditions
Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Aditya S. Ghate
Department of Astronautics and Aeronautics, Stanford University,
Stanford, CA 94305, USA
NASA Ames Research Center, Moffet Field, CA 94035, USA
Jesús Bas Quesada
Siemens Gamesa Renewable Energy Innovation & Technology, Sarriguren, Navarra, 31621, Spain
Juan José Pena Martínez
Siemens Gamesa Renewable Energy Innovation & Technology, Sarriguren, Navarra, 31621, Spain
Wei Zhong
Siemens Gamesa Renewable Energy Innovation & Technology, Sarriguren, Navarra, 31621, Spain
Felipe Palou Larrañaga
Siemens Gamesa Renewable Energy Innovation & Technology, Sarriguren, Navarra, 31621, Spain
Sanjiva K. Lele
Department of Astronautics and Aeronautics, Stanford University,
Stanford, CA 94305, USA
John O. Dabiri
Graduate Aerospace Laboratories (GALCIT), California Institute of Technology, Pasadena, CA 91125, USA
Department of Mechanical and Civil Engineering, California Institute of Technology, Pasadena, CA 91125, USA
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Cited
16 citations as recorded by crossref.
- Wind plant power maximization via extremum seeking yaw control: A wind tunnel experiment D. Kumar et al. 10.1002/we.2799
- 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
- Evaluation of wind resource uncertainty on energy production estimates for offshore wind farms K. Klemmer et al. 10.1063/5.0166830
- Collective wind farm operation based on a predictive model increases utility-scale energy production M. Howland et al. 10.1038/s41560-022-01085-8
- Control-oriented modelling of wind direction variability S. Dallas et al. 10.5194/wes-9-841-2024
- Increased power gains from wake steering control using preview wind direction information B. Sengers et al. 10.5194/wes-8-1693-2023
- Stochastic gradient descent for wind farm optimization J. Quick et al. 10.5194/wes-8-1235-2023
- Impact of yaw misalignment on turbine loads in the presence of wind farm blockage F. Bernardoni et al. 10.1002/we.2899
- Sensitivity analysis of wake steering optimisation for wind farm power maximisation F. Gori et al. 10.5194/wes-8-1425-2023
- Data-driven wake model parameter estimation to analyze effects of wake superposition M. LoCascio et al. 10.1063/5.0163896
- Model‐free closed‐loop wind farm control using reinforcement learning with recursive least squares J. Liew et al. 10.1002/we.2852
- Towards sequential sensor placements on a wind farm to maximize lifetime energy and profit A. Yildiz et al. 10.1016/j.renene.2023.119040
- Measurement-driven large-eddy simulations of a diurnal cycle during a wake-steering field campaign E. Quon 10.5194/wes-9-495-2024
- Modelling the induction, thrust and power of a yaw-misaligned actuator disk K. Heck et al. 10.1017/jfm.2023.129
- Optimal closed-loop wake steering – Part 2: Diurnal cycle atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-7-345-2022
- Parameter Uncertainty Quantification in an Idealized GCM With a Seasonal Cycle M. Howland et al. 10.1029/2021MS002735
14 citations as recorded by crossref.
- Wind plant power maximization via extremum seeking yaw control: A wind tunnel experiment D. Kumar et al. 10.1002/we.2799
- 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
- Evaluation of wind resource uncertainty on energy production estimates for offshore wind farms K. Klemmer et al. 10.1063/5.0166830
- Collective wind farm operation based on a predictive model increases utility-scale energy production M. Howland et al. 10.1038/s41560-022-01085-8
- Control-oriented modelling of wind direction variability S. Dallas et al. 10.5194/wes-9-841-2024
- Increased power gains from wake steering control using preview wind direction information B. Sengers et al. 10.5194/wes-8-1693-2023
- Stochastic gradient descent for wind farm optimization J. Quick et al. 10.5194/wes-8-1235-2023
- Impact of yaw misalignment on turbine loads in the presence of wind farm blockage F. Bernardoni et al. 10.1002/we.2899
- Sensitivity analysis of wake steering optimisation for wind farm power maximisation F. Gori et al. 10.5194/wes-8-1425-2023
- Data-driven wake model parameter estimation to analyze effects of wake superposition M. LoCascio et al. 10.1063/5.0163896
- Model‐free closed‐loop wind farm control using reinforcement learning with recursive least squares J. Liew et al. 10.1002/we.2852
- Towards sequential sensor placements on a wind farm to maximize lifetime energy and profit A. Yildiz et al. 10.1016/j.renene.2023.119040
- Measurement-driven large-eddy simulations of a diurnal cycle during a wake-steering field campaign E. Quon 10.5194/wes-9-495-2024
- Modelling the induction, thrust and power of a yaw-misaligned actuator disk K. Heck et al. 10.1017/jfm.2023.129
Latest update: 25 Apr 2024
Short summary
Wake steering control, in which turbines are intentionally misaligned with the incident wind, has demonstrated potential to increase wind farm energy. We investigate wake steering control methods in simulations of a wind farm operating in the terrestrial diurnal cycle. We develop a statistical wind direction forecast to improve wake steering in flows with time-varying states. Closed-loop wake steering control increases wind farm energy production, compared to baseline and open-loop control.
Wake steering control, in which turbines are intentionally misaligned with the incident wind,...
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