Articles | Volume 5, issue 4
https://doi.org/10.5194/wes-5-1315-2020
© 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-5-1315-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Oct 2020
Research article |
| 13 Oct 2020
| 13 Oct 2020
Optimal closed-loop wake steering – Part 1: Conventionally neutral atmospheric boundary layer conditions
Michael F. Howland
Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA
Aditya S. Ghate
Department of Astronautics and Aeronautics, Stanford University,
Stanford, CA 94305, USA
Sanjiva K. Lele
Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA
Department of Astronautics and Aeronautics, Stanford University,
Stanford, CA 94305, USA
John O. Dabiri
CORRESPONDING AUTHOR
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
33 citations as recorded by crossref.
- Influence of atmospheric conditions on the power production of utility-scale wind turbines in yaw misalignment M. Howland et al. 10.1063/5.0023746
- 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
- Large Eddy Simulation Study of Atmospheric Boundary Layer Flow over an Abrupt Rough-to-Smooth Surface Roughness Transition K. Mondal et al. 10.1007/s10546-023-00811-3
- Optimal closed-loop wake steering – Part 2: Diurnal cycle atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-7-345-2022
- Sensitivity analysis of wake steering optimisation for wind farm power maximisation F. Gori et al. 10.5194/wes-8-1425-2023
- 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
- A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes M. Amiri et al. 10.1016/j.rser.2024.114279
- Wind farm yaw control set-point optimization under model parameter uncertainty M. Howland 10.1063/5.0051071
- Wind farm flow control: prospects and challenges J. Meyers et al. 10.5194/wes-7-2271-2022
- A dynamic model of wind turbine yaw for active farm control G. Starke et al. 10.1002/we.2884
- Mechanisms of dynamic near-wake modulation of a utility-scale wind turbine A. Abraham et al. 10.1017/jfm.2021.737
- Ensemble-Based Flow Field Estimation Using the Dynamic Wind Farm Model FLORIDyn M. Becker et al. 10.3390/en15228589
- Modelling the induction, thrust and power of a yaw-misaligned actuator disk K. Heck et al. 10.1017/jfm.2023.129
- Design and analysis of a wake model for spatially heterogeneous flow A. Farrell et al. 10.5194/wes-6-737-2021
- Adjoint optimisation for wind farm flow control with a free-vortex wake model M. van den Broek et al. 10.1016/j.renene.2022.10.120
- Wind plant power maximization via extremum seeking yaw control: A wind tunnel experiment D. Kumar et al. 10.1002/we.2799
- A physics-based model for wind turbine wake expansion in the atmospheric boundary layer D. Vahidi & F. Porté-Agel 10.1017/jfm.2022.443
- Model‐free closed‐loop wind farm control using reinforcement learning with recursive least squares J. Liew et al. 10.1002/we.2852
- Data-driven wake model parameter estimation to analyze effects of wake superposition M. LoCascio et al. 10.1063/5.0163896
- Parameter Uncertainty Quantification in an Idealized GCM With a Seasonal Cycle M. Howland et al. 10.1029/2021MS002735
- Dynamic wind farm flow control using free-vortex wake models M. van den Broek et al. 10.5194/wes-9-721-2024
- Experimental and numerical investigation on the potential of wake mixing by dynamic yaw for wind farm power optimization F. Mühle et al. 10.1088/1742-6596/2767/9/092068
- Large Eddy Simulation of wind turbine fatigue loading and yaw dynamics induced by wake turbulence W. Chanprasert et al. 10.1016/j.renene.2022.03.097
- Review of wake management techniques for wind turbines D. Houck 10.1002/we.2668
- Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment B. Sengers et al. 10.5194/wes-8-747-2023
- Identification of wind turbine clusters for effective real time yaw control optimization F. Bernardoni et al. 10.1063/5.0036640
- Wind Farm Power Maximisation via Wake Steering: A Gaussian Process‐Based Yaw‐Dependent Parameter Tuning Approach F. Gori et al. 10.1002/we.2953
- Comparison of helix and wake steering control for varying turbine spacing and wind direction E. Taschner et al. 10.1088/1742-6596/2767/3/032023
- The characteristics of helically deflected wind turbine wakes H. Korb et al. 10.1017/jfm.2023.390
- Collective wind farm operation based on a predictive model increases utility-scale energy production M. Howland et al. 10.1038/s41560-022-01085-8
- Influence of Wake Model Superposition and Secondary Steering on Model-Based Wake Steering Control with SCADA Data Assimilation M. Howland & J. Dabiri 10.3390/en14010052
- 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
- Optimal closed-loop wake steering – Part 1: Conventionally neutral atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-5-1315-2020
31 citations as recorded by crossref.
- Influence of atmospheric conditions on the power production of utility-scale wind turbines in yaw misalignment M. Howland et al. 10.1063/5.0023746
- 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
- Large Eddy Simulation Study of Atmospheric Boundary Layer Flow over an Abrupt Rough-to-Smooth Surface Roughness Transition K. Mondal et al. 10.1007/s10546-023-00811-3
- Optimal closed-loop wake steering – Part 2: Diurnal cycle atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-7-345-2022
- Sensitivity analysis of wake steering optimisation for wind farm power maximisation F. Gori et al. 10.5194/wes-8-1425-2023
- 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
- A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes M. Amiri et al. 10.1016/j.rser.2024.114279
- Wind farm yaw control set-point optimization under model parameter uncertainty M. Howland 10.1063/5.0051071
- Wind farm flow control: prospects and challenges J. Meyers et al. 10.5194/wes-7-2271-2022
- A dynamic model of wind turbine yaw for active farm control G. Starke et al. 10.1002/we.2884
- Mechanisms of dynamic near-wake modulation of a utility-scale wind turbine A. Abraham et al. 10.1017/jfm.2021.737
- Ensemble-Based Flow Field Estimation Using the Dynamic Wind Farm Model FLORIDyn M. Becker et al. 10.3390/en15228589
- Modelling the induction, thrust and power of a yaw-misaligned actuator disk K. Heck et al. 10.1017/jfm.2023.129
- Design and analysis of a wake model for spatially heterogeneous flow A. Farrell et al. 10.5194/wes-6-737-2021
- Adjoint optimisation for wind farm flow control with a free-vortex wake model M. van den Broek et al. 10.1016/j.renene.2022.10.120
- Wind plant power maximization via extremum seeking yaw control: A wind tunnel experiment D. Kumar et al. 10.1002/we.2799
- A physics-based model for wind turbine wake expansion in the atmospheric boundary layer D. Vahidi & F. Porté-Agel 10.1017/jfm.2022.443
- Model‐free closed‐loop wind farm control using reinforcement learning with recursive least squares J. Liew et al. 10.1002/we.2852
- Data-driven wake model parameter estimation to analyze effects of wake superposition M. LoCascio et al. 10.1063/5.0163896
- Parameter Uncertainty Quantification in an Idealized GCM With a Seasonal Cycle M. Howland et al. 10.1029/2021MS002735
- Dynamic wind farm flow control using free-vortex wake models M. van den Broek et al. 10.5194/wes-9-721-2024
- Experimental and numerical investigation on the potential of wake mixing by dynamic yaw for wind farm power optimization F. Mühle et al. 10.1088/1742-6596/2767/9/092068
- Large Eddy Simulation of wind turbine fatigue loading and yaw dynamics induced by wake turbulence W. Chanprasert et al. 10.1016/j.renene.2022.03.097
- Review of wake management techniques for wind turbines D. Houck 10.1002/we.2668
- Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment B. Sengers et al. 10.5194/wes-8-747-2023
- Identification of wind turbine clusters for effective real time yaw control optimization F. Bernardoni et al. 10.1063/5.0036640
- Wind Farm Power Maximisation via Wake Steering: A Gaussian Process‐Based Yaw‐Dependent Parameter Tuning Approach F. Gori et al. 10.1002/we.2953
- Comparison of helix and wake steering control for varying turbine spacing and wind direction E. Taschner et al. 10.1088/1742-6596/2767/3/032023
- The characteristics of helically deflected wind turbine wakes H. Korb et al. 10.1017/jfm.2023.390
- Collective wind farm operation based on a predictive model increases utility-scale energy production M. Howland et al. 10.1038/s41560-022-01085-8
- Influence of Wake Model Superposition and Secondary Steering on Model-Based Wake Steering Control with SCADA Data Assimilation M. Howland & J. Dabiri 10.3390/en14010052
2 citations as recorded by crossref.
- 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
- Optimal closed-loop wake steering – Part 1: Conventionally neutral atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-5-1315-2020
Latest update: 20 Nov 2024
Short summary
Wake losses significantly reduce the power production of utility-scale wind farms since all wind turbines are operated in a greedy, individual power maximization fashion. In order to mitigate wake losses, collective wind farm operation strategies use wake steering, in which certain turbines are intentionally misaligned with respect to the incoming wind direction. The control strategy developed is dynamic and closed-loop to adapt to changing atmospheric conditions.
Wake losses significantly reduce the power production of utility-scale wind farms since all wind...
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