Articles | Volume 7, issue 6
https://doi.org/10.5194/wes-7-2271-2022
© 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-7-2271-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
25 Nov 2022
Review article |
| 25 Nov 2022
| 25 Nov 2022
Wind farm flow control: prospects and challenges
KU Leuven, Mechanical Engineering, Celestijnenlaan 300A, B3001 Leuven, Belgium
Carlo Bottasso
Chair of Wind Energy, Technische Universität München, Boltzmannstr. 15, 85748 Garching b. München, Germany
Katherine Dykes
DTU Wind Energy, Frederiksborgvej 399, 4000 Roskilde, Denmark
Paul Fleming
National Renewable Energy Laboratory, Boulder, Colorado, USA
Pieter Gebraad
Siemens Gamesa Renewable Energy, Tonsbakken 16, 2740 Skovlunde, Denmark
Gregor Giebel
DTU Wind Energy, Frederiksborgvej 399, 4000 Roskilde, Denmark
Tuhfe Göçmen
DTU Wind Energy, Frederiksborgvej 399, 4000 Roskilde, Denmark
Jan-Willem van Wingerden
Delft University of Technology, Delft Center for Systems and Control, Mekelweg 2, 2628 CD Delft, the Netherlands
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- On the performance of the helix wind farm control approach in the conventionally neutral atmospheric boundary layer E. Taschner et al. 10.1088/1742-6596/2505/1/012006
- Unified momentum model for rotor aerodynamics across operating regimes J. Liew et al. 10.1038/s41467-024-50756-5
- On the impact of different static induction control strategies on a wind turbine wake M. Zúñiga Inestroza et al. 10.1088/1742-6596/2767/9/092082
- Surrogate Modeling and Aeroelastic Analysis of a Wind Turbine with Down-Regulation, Power Boosting, and IBC Capabilities V. Pettas & P. Cheng 10.3390/en17061284
- 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
- Maximizing wind farm power output with the helix approach: Experimental validation and wake analysis using tomographic particle image velocimetry D. van der Hoek et al. 10.1002/we.2896
- Increased power gains from wake steering control using preview wind direction information B. Sengers et al. 10.5194/wes-8-1693-2023
- Physics-Guided Machine Learning for Wind-Farm Power Prediction: Toward Interpretability and Generalizability N. Zehtabiyan-Rezaie et al. 10.1103/PRXEnergy.2.013009
- Study on the yaw-based wake steering control considering dynamic flow characteristics for wind farm power improvement X. Yu et al. 10.1088/1742-6596/2505/1/012010
- 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
- Comparison of steady-state analytical wake models implemented in wind farm analysis software R. Mudafort et al. 10.1088/1742-6596/2767/5/052066
- Wind Farm Power Maximisation via Wake Steering: A Gaussian Process‐Based Yaw‐Dependent Parameter Tuning Approach F. Gori et al. 10.1002/we.2953
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Short summary
We provide a comprehensive overview of the state of the art and the outstanding challenges in wind farm flow control, thus identifying the key research areas that could further enable commercial uptake and success. To this end, we have structured the discussion on challenges and opportunities into four main areas: (1) insight into control flow physics, (2) algorithms and AI, (3) validation and industry implementation, and (4) integrating control with system design
(co-design).
We provide a comprehensive overview of the state of the art and the outstanding challenges in...
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