Articles | Volume 5, issue 1
Wind Energ. Sci., 5, 245–257, 2020
https://doi.org/10.5194/wes-5-245-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue: Wind Energy Science Conference 2019
Research article
21 Feb 2020
Research article
| 21 Feb 2020
Periodic dynamic induction control of wind farms: proving the potential in simulations and wind tunnel experiments
Joeri Alexis Frederik et al.
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Cited
19 citations as recorded by crossref.
- On the Potential of Reduced Order Models for Wind Farm Control: A Koopman Dynamic Mode Decomposition Approach N. Cassamo & J. van Wingerden 10.3390/en13246513
- Set-point optimization in wind farms to mitigate effects of flow blockage induced by atmospheric gravity waves L. Lanzilao & J. Meyers 10.5194/wes-6-247-2021
- Load reduction for wind turbines: an output-constrained, subspace predictive repetitive control approach Y. Liu et al. 10.5194/wes-7-523-2022
- Optimal closed-loop wake steering – Part 1: Conventionally neutral atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-5-1315-2020
- The helix approach: Using dynamic individual pitch control to enhance wake mixing in wind farms J. Frederik et al. 10.1002/we.2513
- Wind farm flow control oriented to electricity markets and grid integration: Initial perspective analysis I. Eguinoa et al. 10.1002/adc2.80
- Mechanisms of dynamic near-wake modulation of a utility-scale wind turbine A. Abraham et al. 10.1017/jfm.2021.737
- Exploring the application of reinforcement learning to wind farm control H. Korb et al. 10.1088/1742-6596/1934/1/012022
- Evaluation of the impact of active wake control techniques on ultimate loads for a 10 MW wind turbine A. Croce et al. 10.5194/wes-7-1-2022
- How realistic are the wakes of scaled wind turbine models? C. Wang et al. 10.5194/wes-6-961-2021
- Identification of airfoil polars from uncertain experimental measurements C. Wang et al. 10.5194/wes-5-1537-2020
- Review of wake management techniques for wind turbines D. Houck 10.1002/we.2668
- Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm M. van Beek et al. 10.3390/en14051293
- Wind turbine drivetrains: state-of-the-art technologies and future development trends A. Nejad et al. 10.5194/wes-7-387-2022
- On the scaling of wind turbine rotors H. Canet et al. 10.5194/wes-6-601-2021
- Wind tunnel testing of wake steering with dynamic wind direction changes F. Campagnolo et al. 10.5194/wes-5-1273-2020
- Surrogate Models for Wind Turbine Electrical Power and Fatigue Loads in Wind Farm G. Gasparis et al. 10.3390/en13236360
- Expert Elicitation on Wind Farm Control J. van Wingerden et al. 10.1088/1742-6596/1618/2/022025
- Accelerated Wind-Turbine Wake Recovery Through Actuation of the Tip-Vortex Instability K. Brown et al. 10.2514/1.J060772
19 citations as recorded by crossref.
- On the Potential of Reduced Order Models for Wind Farm Control: A Koopman Dynamic Mode Decomposition Approach N. Cassamo & J. van Wingerden 10.3390/en13246513
- Set-point optimization in wind farms to mitigate effects of flow blockage induced by atmospheric gravity waves L. Lanzilao & J. Meyers 10.5194/wes-6-247-2021
- Load reduction for wind turbines: an output-constrained, subspace predictive repetitive control approach Y. Liu et al. 10.5194/wes-7-523-2022
- Optimal closed-loop wake steering – Part 1: Conventionally neutral atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-5-1315-2020
- The helix approach: Using dynamic individual pitch control to enhance wake mixing in wind farms J. Frederik et al. 10.1002/we.2513
- Wind farm flow control oriented to electricity markets and grid integration: Initial perspective analysis I. Eguinoa et al. 10.1002/adc2.80
- Mechanisms of dynamic near-wake modulation of a utility-scale wind turbine A. Abraham et al. 10.1017/jfm.2021.737
- Exploring the application of reinforcement learning to wind farm control H. Korb et al. 10.1088/1742-6596/1934/1/012022
- Evaluation of the impact of active wake control techniques on ultimate loads for a 10 MW wind turbine A. Croce et al. 10.5194/wes-7-1-2022
- How realistic are the wakes of scaled wind turbine models? C. Wang et al. 10.5194/wes-6-961-2021
- Identification of airfoil polars from uncertain experimental measurements C. Wang et al. 10.5194/wes-5-1537-2020
- Review of wake management techniques for wind turbines D. Houck 10.1002/we.2668
- Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm M. van Beek et al. 10.3390/en14051293
- Wind turbine drivetrains: state-of-the-art technologies and future development trends A. Nejad et al. 10.5194/wes-7-387-2022
- On the scaling of wind turbine rotors H. Canet et al. 10.5194/wes-6-601-2021
- Wind tunnel testing of wake steering with dynamic wind direction changes F. Campagnolo et al. 10.5194/wes-5-1273-2020
- Surrogate Models for Wind Turbine Electrical Power and Fatigue Loads in Wind Farm G. Gasparis et al. 10.3390/en13236360
- Expert Elicitation on Wind Farm Control J. van Wingerden et al. 10.1088/1742-6596/1618/2/022025
- Accelerated Wind-Turbine Wake Recovery Through Actuation of the Tip-Vortex Instability K. Brown et al. 10.2514/1.J060772
Latest update: 16 May 2022
Short summary
The interaction between wind turbines in a wind farm through their wakes is a widely studied research area. Until recently, research was focused on finding constant turbine inputs that optimize the performance of the wind farm. However, recent studies have shown that time-varying, dynamic inputs might be more beneficial. In this paper, the validity of this approach is further investigated by implementing it in scaled wind tunnel experiments and assessing load effects, showing promising results.
The interaction between wind turbines in a wind farm through their wakes is a widely studied...
Special issue