Articles | Volume 5, issue 1
https://doi.org/10.5194/wes-5-413-2020
© 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-5-413-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Mar 2020
Research article |
| 30 Mar 2020
| 30 Mar 2020
Wake steering optimization under uncertainty
University of Colorado, Boulder, CO, USA
National Renewable Energy Laboratory, Golden, CO, USA
Jennifer King
National Renewable Energy Laboratory, Golden, CO, USA
Ryan N. King
National Renewable Energy Laboratory, Golden, CO, USA
Peter E. Hamlington
University of Colorado, Boulder, CO, USA
Katherine Dykes
National Renewable Energy Laboratory, Golden, CO, USA
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Cited
24 citations as recorded by crossref.
- Wind farm yaw control set-point optimization under model parameter uncertainty M. Howland 10.1063/5.0051071
- A Probabilistic Learning Approach Applied to the Optimization of Wake Steering in Wind Farms J. Almeida & F. Rochinha 10.1115/1.4054501
- Wind farm flow control: prospects and challenges J. Meyers et al. 10.5194/wes-7-2271-2022
- Wind Farm Yield and Lifetime Optimization by Smart Steering of Wakes J. Schmidt et al. 10.1088/1742-6596/1934/1/012020
- A Machine Learning Method for Modeling Wind Farm Fatigue Load Y. Miao et al. 10.3390/app12157392
- Review of wake management techniques for wind turbines D. Houck 10.1002/we.2668
- FarmConners market showcase results: wind farm flow control considering electricity prices K. Kölle et al. 10.5194/wes-7-2181-2022
- 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
- Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm M. van Beek et al. 10.3390/en14051293
- Multifidelity multiobjective optimization for wake-steering strategies J. Quick et al. 10.5194/wes-7-1941-2022
- Probabilistic surrogates for flow control using combined control strategies C. Debusscher et al. 10.1088/1742-6596/2265/3/032110
- Quantitative assessment on fatigue damage induced by wake effect and yaw misalignment for floating offshore wind turbines T. Tao et al. 10.1016/j.oceaneng.2023.116004
- Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets S. De et al. 10.1007/s00466-023-02272-4
- Optimal closed-loop wake steering – Part 2: Diurnal cycle atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-7-345-2022
- A Numerical Investigation of the Geometric Characteristics of Floating Wind Turbine Wakes under Axial and Yawed Rotor Conditions J. Valentin & T. Sant 10.1088/1742-6596/2018/1/012044
- Data-driven wake model parameter estimation to analyze effects of wake superposition M. LoCascio et al. 10.1063/5.0163896
- Fast yaw optimization for wind plant wake steering using Boolean yaw angles A. Stanley et al. 10.5194/wes-7-741-2022
- Experimental results of wake steering using fixed angles P. Fleming et al. 10.5194/wes-6-1521-2021
- Stochastic gradient descent for wind farm optimization J. Quick et al. 10.5194/wes-8-1235-2023
- Study of a dynamic effect-based method for wind farm yaw control optimization L. Li et al. 10.1080/15435075.2023.2297771
- Control-oriented modelling of wind direction variability S. Dallas et al. 10.5194/wes-9-841-2024
- Time-domain fatigue damage assessment for wind turbine tower bolts under yaw optimization control at offshore wind farm T. Tao et al. 10.1016/j.oceaneng.2024.117706
- Review of Mesoscale Wind-Farm Parametrizations and Their Applications J. Fischereit et al. 10.1007/s10546-021-00652-y
24 citations as recorded by crossref.
- Wind farm yaw control set-point optimization under model parameter uncertainty M. Howland 10.1063/5.0051071
- A Probabilistic Learning Approach Applied to the Optimization of Wake Steering in Wind Farms J. Almeida & F. Rochinha 10.1115/1.4054501
- Wind farm flow control: prospects and challenges J. Meyers et al. 10.5194/wes-7-2271-2022
- Wind Farm Yield and Lifetime Optimization by Smart Steering of Wakes J. Schmidt et al. 10.1088/1742-6596/1934/1/012020
- A Machine Learning Method for Modeling Wind Farm Fatigue Load Y. Miao et al. 10.3390/app12157392
- Review of wake management techniques for wind turbines D. Houck 10.1002/we.2668
- FarmConners market showcase results: wind farm flow control considering electricity prices K. Kölle et al. 10.5194/wes-7-2181-2022
- 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
- Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm M. van Beek et al. 10.3390/en14051293
- Multifidelity multiobjective optimization for wake-steering strategies J. Quick et al. 10.5194/wes-7-1941-2022
- Probabilistic surrogates for flow control using combined control strategies C. Debusscher et al. 10.1088/1742-6596/2265/3/032110
- Quantitative assessment on fatigue damage induced by wake effect and yaw misalignment for floating offshore wind turbines T. Tao et al. 10.1016/j.oceaneng.2023.116004
- Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets S. De et al. 10.1007/s00466-023-02272-4
- Optimal closed-loop wake steering – Part 2: Diurnal cycle atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-7-345-2022
- A Numerical Investigation of the Geometric Characteristics of Floating Wind Turbine Wakes under Axial and Yawed Rotor Conditions J. Valentin & T. Sant 10.1088/1742-6596/2018/1/012044
- Data-driven wake model parameter estimation to analyze effects of wake superposition M. LoCascio et al. 10.1063/5.0163896
- Fast yaw optimization for wind plant wake steering using Boolean yaw angles A. Stanley et al. 10.5194/wes-7-741-2022
- Experimental results of wake steering using fixed angles P. Fleming et al. 10.5194/wes-6-1521-2021
- Stochastic gradient descent for wind farm optimization J. Quick et al. 10.5194/wes-8-1235-2023
- Study of a dynamic effect-based method for wind farm yaw control optimization L. Li et al. 10.1080/15435075.2023.2297771
- Control-oriented modelling of wind direction variability S. Dallas et al. 10.5194/wes-9-841-2024
- Time-domain fatigue damage assessment for wind turbine tower bolts under yaw optimization control at offshore wind farm T. Tao et al. 10.1016/j.oceaneng.2024.117706
- Review of Mesoscale Wind-Farm Parametrizations and Their Applications J. Fischereit et al. 10.1007/s10546-021-00652-y
Latest update: 19 Apr 2024
Short summary
We investigate the trade-offs in optimization of wake steering strategies, where upstream turbines are positioned to deflect wakes away from downstream turbines, with a probabilistic perspective. We identify inputs that are sensitive to uncertainty and demonstrate a realistic optimization under uncertainty for a wind power plant control strategy. Designing explicitly around uncertainty yielded control strategies that were generally less aggressive and more robust to the uncertain input.
We investigate the trade-offs in optimization of wake steering strategies, where upstream...
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