Articles | Volume 6, issue 2
https://doi.org/10.5194/wes-6-389-2021
© Author(s) 2021. 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-6-389-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Mar 2021
Research article |
| 15 Mar 2021
| 15 Mar 2021
Axial induction controller field test at Sedini wind farm
Ervin Bossanyi
CORRESPONDING AUTHOR
DNV, One Linear Park, Avon Street, Bristol, BS2 0PS, UK
Renzo Ruisi
DNV, One Linear Park, Avon Street, Bristol, BS2 0PS, UK
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Cited
29 citations as recorded by crossref.
- Control-oriented modelling of wind direction variability S. Dallas et al. https://doi.org/10.5194/wes-9-841-2024
- FarmConners wind farm flow control benchmark – Part 1: Blind test results T. Göçmen et al. https://doi.org/10.5194/wes-7-1791-2022
- Comparison of the Gaussian Wind Farm Model with Historical Data of Three Offshore Wind Farms B. Doekemeijer et al. https://doi.org/10.3390/en15061964
- Experimental comparison of induction control methods for wind farm power maximization on a scaled two-turbine setup D. Van Der Hoek et al. https://doi.org/10.1088/1742-6596/2767/9/092064
- Reinforcement Learning-based Wake Steering with Wind Direction Preview for Power Enhancement M. Wang et al. https://doi.org/10.1088/1742-6596/3224/3/032066
- The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data R. Braunbehrens et al. https://doi.org/10.5194/wes-8-691-2023
- Data-driven wind farm flow control and challenges towards field implementation: A review T. Göçmen et al. https://doi.org/10.1016/j.rser.2025.115605
- Probabilistic surrogates for flow control using combined control strategies C. Debusscher et al. https://doi.org/10.1088/1742-6596/2265/3/032110
- Validating wind farm flow control uplift calculation methodologies using simulated test data M. Harrison & L. Landberg https://doi.org/10.1088/1742-6596/3224/3/032068
- Power and Flow Analysis of Axial Induction Control in an Array of Model-Scale Wind Turbines D. Houck & E. Cowen https://doi.org/10.3390/en15155347
- Feasibility Study of implementing Wake Steering in Floating Wind Farms: Power Gain, Load Estimation and Economic Analysis M. Mohammadi et al. https://doi.org/10.1002/we.70111
- Stochastic Dynamical Modeling of Wind Farm Turbulence A. Bhatt et al. https://doi.org/10.3390/en16196908
- Modular deep learning approach for wind farm power forecasting and wake loss prediction S. Ally et al. https://doi.org/10.5194/wes-10-779-2025
- Wind farm power maximization based on analytical sensitivity model considering wake effect C. Xu et al. https://doi.org/10.1016/j.epsr.2023.109734
- Time-Accurate Wind Farm Control in Dynamic Flow Conditions using Deep Reinforcement Learning H. Sheehan et al. https://doi.org/10.1088/1742-6596/3016/1/012025
- Review of wake management techniques for wind turbines D. Houck https://doi.org/10.1002/we.2668
- Turbulence and Control of Wind Farms C. Shapiro et al. https://doi.org/10.1146/annurev-control-070221-114032
- Comparison of wind farm control strategies under realistic offshore wind conditions: turbine quantities of interest J. Frederik et al. https://doi.org/10.5194/wes-10-755-2025
- Graph-based Deep Reinforcement Learning for Wind Farm Set-Point Optimisation H. Sheehan et al. https://doi.org/10.1088/1742-6596/2767/9/092028
- On the impact of different static induction control strategies on a wind turbine wake M. Zúñiga Inestroza et al. https://doi.org/10.1088/1742-6596/2767/9/092082
- Scaled testing of maximum-reserve active power control S. Tamaro et al. https://doi.org/10.5194/wes-11-1607-2026
- The impact of coherent large-scale vortices generated by helix active wake control on the recovery process of wind turbine wakes J. Gutknecht et al. https://doi.org/10.1063/5.0278687
- Combining wake redirection and derating strategies in a load-constrained wind farm power maximization A. Croce et al. https://doi.org/10.5194/wes-9-1211-2024
- Revenue-Focused Wind Farm Control Co-Design for Future Electricity Markets Scenarios D. Dirik et al. https://doi.org/10.1088/1742-6596/3016/1/012024
- Hyperparameter tuning framework for calibrating analytical wake models using SCADA data of an offshore wind farm D. van Binsbergen et al. https://doi.org/10.5194/wes-9-1507-2024
- Numerical modelling of offshore wind-farm cluster wakes P. Ouro et al. https://doi.org/10.1016/j.rser.2025.115526
- Validating the next generation of turbine interaction models T. Levick et al. https://doi.org/10.1088/1742-6596/2257/1/012010
- Uncertainty of toggling in wake steering experiments under diurnal cycle atmospheric conditions: an LES study E. Hodgson et al. https://doi.org/10.1088/1742-6596/3016/1/012026
- Wind farm flow control: prospects and challenges J. Meyers et al. https://doi.org/10.5194/wes-7-2271-2022
29 citations as recorded by crossref.
- Control-oriented modelling of wind direction variability S. Dallas et al. https://doi.org/10.5194/wes-9-841-2024
- FarmConners wind farm flow control benchmark – Part 1: Blind test results T. Göçmen et al. https://doi.org/10.5194/wes-7-1791-2022
- Comparison of the Gaussian Wind Farm Model with Historical Data of Three Offshore Wind Farms B. Doekemeijer et al. https://doi.org/10.3390/en15061964
- Experimental comparison of induction control methods for wind farm power maximization on a scaled two-turbine setup D. Van Der Hoek et al. https://doi.org/10.1088/1742-6596/2767/9/092064
- Reinforcement Learning-based Wake Steering with Wind Direction Preview for Power Enhancement M. Wang et al. https://doi.org/10.1088/1742-6596/3224/3/032066
- The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data R. Braunbehrens et al. https://doi.org/10.5194/wes-8-691-2023
- Data-driven wind farm flow control and challenges towards field implementation: A review T. Göçmen et al. https://doi.org/10.1016/j.rser.2025.115605
- Probabilistic surrogates for flow control using combined control strategies C. Debusscher et al. https://doi.org/10.1088/1742-6596/2265/3/032110
- Validating wind farm flow control uplift calculation methodologies using simulated test data M. Harrison & L. Landberg https://doi.org/10.1088/1742-6596/3224/3/032068
- Power and Flow Analysis of Axial Induction Control in an Array of Model-Scale Wind Turbines D. Houck & E. Cowen https://doi.org/10.3390/en15155347
- Feasibility Study of implementing Wake Steering in Floating Wind Farms: Power Gain, Load Estimation and Economic Analysis M. Mohammadi et al. https://doi.org/10.1002/we.70111
- Stochastic Dynamical Modeling of Wind Farm Turbulence A. Bhatt et al. https://doi.org/10.3390/en16196908
- Modular deep learning approach for wind farm power forecasting and wake loss prediction S. Ally et al. https://doi.org/10.5194/wes-10-779-2025
- Wind farm power maximization based on analytical sensitivity model considering wake effect C. Xu et al. https://doi.org/10.1016/j.epsr.2023.109734
- Time-Accurate Wind Farm Control in Dynamic Flow Conditions using Deep Reinforcement Learning H. Sheehan et al. https://doi.org/10.1088/1742-6596/3016/1/012025
- Review of wake management techniques for wind turbines D. Houck https://doi.org/10.1002/we.2668
- Turbulence and Control of Wind Farms C. Shapiro et al. https://doi.org/10.1146/annurev-control-070221-114032
- Comparison of wind farm control strategies under realistic offshore wind conditions: turbine quantities of interest J. Frederik et al. https://doi.org/10.5194/wes-10-755-2025
- Graph-based Deep Reinforcement Learning for Wind Farm Set-Point Optimisation H. Sheehan et al. https://doi.org/10.1088/1742-6596/2767/9/092028
- On the impact of different static induction control strategies on a wind turbine wake M. Zúñiga Inestroza et al. https://doi.org/10.1088/1742-6596/2767/9/092082
- Scaled testing of maximum-reserve active power control S. Tamaro et al. https://doi.org/10.5194/wes-11-1607-2026
- The impact of coherent large-scale vortices generated by helix active wake control on the recovery process of wind turbine wakes J. Gutknecht et al. https://doi.org/10.1063/5.0278687
- Combining wake redirection and derating strategies in a load-constrained wind farm power maximization A. Croce et al. https://doi.org/10.5194/wes-9-1211-2024
- Revenue-Focused Wind Farm Control Co-Design for Future Electricity Markets Scenarios D. Dirik et al. https://doi.org/10.1088/1742-6596/3016/1/012024
- Hyperparameter tuning framework for calibrating analytical wake models using SCADA data of an offshore wind farm D. van Binsbergen et al. https://doi.org/10.5194/wes-9-1507-2024
- Numerical modelling of offshore wind-farm cluster wakes P. Ouro et al. https://doi.org/10.1016/j.rser.2025.115526
- Validating the next generation of turbine interaction models T. Levick et al. https://doi.org/10.1088/1742-6596/2257/1/012010
- Uncertainty of toggling in wake steering experiments under diurnal cycle atmospheric conditions: an LES study E. Hodgson et al. https://doi.org/10.1088/1742-6596/3016/1/012026
- Wind farm flow control: prospects and challenges J. Meyers et al. https://doi.org/10.5194/wes-7-2271-2022
Saved (final revised paper)
Latest update: 13 Jun 2026
Short summary
This paper describes the design and field testing of a controller for reducing wake interactions on a wind farm. Reducing the power of some turbines weakens their wakes, allowing other turbines to produce more power so that the total wind farm power may increase. There have been doubts that this is feasible, but these field tests on a full-scale wind farm indicate that this goal has been achieved, also providing convincing validation of the model used for designing the controller.
This paper describes the design and field testing of a controller for reducing wake interactions...
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