179 citations as recorded by crossref.

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13. 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
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21. Fluid-Dynamic Mechanisms Underlying Wind Turbine Wake Control with Strouhal-Timed Actuation L. Cheung et al. https://doi.org/10.3390/en17040865
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25. Prediction and Mitigation of Wind Farm Blockage Losses Considering Mesoscale Atmospheric Response L. Legris et al. https://doi.org/10.3390/en16010386
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27. Wake Mixing Control For Floating Wind Farms: Analysis of the Implementation of the Helix Wake Mixing Strategy on the IEA 15-MW Floating Wind Turbine D. van den Berg et al. https://doi.org/10.1109/MCS.2024.3432341
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29. The value of wake steering wind farm flow control in US energy markets E. Simley et al. https://doi.org/10.5194/wes-9-219-2024
30. Evaluating the potential of a wake steering co-design for wind farm layout optimization through a tailored genetic algorithm M. Baricchio et al. https://doi.org/10.5194/wes-9-2113-2024
31. Wind pattern clustering of high frequent field measurements for dynamic wind farm flow control M. Becker et al. https://doi.org/10.1088/1742-6596/2767/3/032028
32. PhyWakeNet: a dynamic wake model accounting for aerodynamic force oscillations X. Liu et al. https://doi.org/10.5194/wes-11-771-2026
33. Discrete Switching Sequence Control for Universal Current Tracking in Wind Power Converters J. Yu et al. https://doi.org/10.3390/electronics14234608
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35. Wake development in floating wind turbines: new insights and an open dataset from wind tunnel experiments A. Fontanella et al. https://doi.org/10.5194/wes-10-1369-2025
36. Experimental investigation of the effects of floating wind turbine motion on a downstream turbine performance and loads A. Fontanella et al. https://doi.org/10.5194/wes-11-1821-2026
37. Scalable SCADA-Based Calibration for Analytical Wake Models Across an Offshore Cluster D. Binsbergen et al. https://doi.org/10.1088/1742-6596/2745/1/012014
38. Analytical solutions for yawed wind-turbine wakes with application to wind-farm power optimization by active yaw control Z. Zhang et al. https://doi.org/10.1016/j.oceaneng.2024.117691
39. Steady-state evaluation of fatigue loads for the helix wake-mixing control method D. Hoek et al. https://doi.org/10.1088/1742-6596/3224/3/032102
40. A database of hourly wind speed and modeled generation for US wind plants based on three meteorological models D. Millstein et al. https://doi.org/10.1038/s41597-023-02804-w
41. Free-vortex models for wind turbine wakes under yaw misalignment – a validation study on far-wake effects M. van den Broek et al. https://doi.org/10.5194/wes-8-1909-2023
42. LES-based validation of a dynamic wind farm flow model under unsteady inflow and yaw misalignment J. Bohrer et al. https://doi.org/10.1088/1742-6596/2767/3/032041
43. Control co-design of a large offshore wind farm considering the effect of wind extractability M. Pahus et al. https://doi.org/10.1088/1742-6596/2767/9/092026
44. Stochastic Dynamical Modeling of Wind Farm Turbulence A. Bhatt et al. https://doi.org/10.3390/en16196908
45. Design of a robotic platform for hybrid wind tunnel experiments of floating wind farms A. Fontanella et al. https://doi.org/10.1088/1742-6596/2767/4/042008
46. LiDAR-Referenced Inflow Wind Condition Estimation from SCADA Data Using a Deep Learning Model S. He et al. https://doi.org/10.3390/en19051373
47. A multi-fidelity approach for wind farm simulations and comparison with field data W. Yu et al. https://doi.org/10.1088/1742-6596/2767/5/052039
48. An open-source framework for the development, deployment and testing of wind farm control strategies C. Sucameli et al. https://doi.org/10.1088/1742-6596/2767/9/092043
49. Measurement-driven large-eddy simulations of a diurnal cycle during a wake-steering field campaign E. Quon https://doi.org/10.5194/wes-9-495-2024
50. Influence of simple terrain on the spatial variability of a low-level jet and wind farm performance in the AWAKEN field campaign W. Radünz et al. https://doi.org/10.5194/wes-10-2365-2025
51. A Supervisory Control Framework for Fatigue-Aware Wake Steering in Wind Farms Y. Shen et al. https://doi.org/10.3390/en18133452
52. On the performance of the helix wind farm control approach in the conventionally neutral atmospheric boundary layer E. Taschner et al. https://doi.org/10.1088/1742-6596/2505/1/012006
53. Phase controlling the yaw motion of floating wind turbines with the helix method to reduce wake interactions: an experimental investigation D. van den Berg et al. https://doi.org/10.5194/wes-11-679-2026
54. Optimization of the Capacity Value of Wind Farms using Windfarm Cluster Control U. Fechner & S. Watson https://doi.org/10.1088/1742-6596/3224/3/032045
55. 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
56. Experimental and numerical investigation on the potential of wake mixing by dynamic yaw for wind farm power optimization F. Mühle et al. https://doi.org/10.1088/1742-6596/2767/9/092068
57. Comparison of helix and wake steering control for varying turbine spacing and wind direction E. Taschner et al. https://doi.org/10.1088/1742-6596/2767/3/032023
58. Comparison of steady-state analytical wake models implemented in wind farm analysis software R. Mudafort et al. https://doi.org/10.1088/1742-6596/2767/5/052066
59. Self-consistent model for active control of wind turbine wakes Z. Li & X. Yang https://doi.org/10.1017/jfm.2025.10263
60. Dynamic wind farm flow control using free-vortex wake models M. van den Broek et al. https://doi.org/10.5194/wes-9-721-2024
61. Deep reinforcement learning-driven wind farm flow control considering dynamic wind H. Wang et al. https://doi.org/10.1016/j.enconman.2025.119888
62. Experimental Analysis of Wakes in Floating Wind Turbines Under Dynamic Induction Control A. Fontanella et al. https://doi.org/10.1088/1742-6596/3131/1/012010
63. How do wind farm layout design, control and co-design optimization compare in mitigating external and internal wake effects? S. Kainz et al. https://doi.org/10.1088/1742-6596/3224/3/032039
64. Evolution and instability of the tip vortices behind a yawed wind turbine C. Li et al. https://doi.org/10.1017/jfm.2025.10389
65. 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
66. On the robustness of a blade-load-based wind speed estimator to dynamic pitch control strategies M. Coquelet et al. https://doi.org/10.5194/wes-9-1923-2024
67. Dynamic response of a shallow conventionally neutral atmospheric boundary layer to active cluster wake control J. Gutknecht et al. https://doi.org/10.1088/1742-6596/3224/3/032123
68. Unsteady aerodynamic loads on pitching aerofoils represented by Gaussian body force distributions E. Taschner et al. https://doi.org/10.1017/jfm.2025.10870
69. Effect of floating wind turbine wakes on the thrust dynamics of a downstream turbine M. Miroux et al. https://doi.org/10.1088/1742-6596/3224/8/082004
70. Positive effect of wind farm control on energy production and revenue in European markets F. Wasilczuk et al. https://doi.org/10.1016/j.energy.2025.136159
71. Wake turbulence modeling in stratified atmospheric flows using a novel k−ℓ model K. Klemmer & M. Howland https://doi.org/10.1063/5.0249278
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73. Synergising Wake Steering and Dynamic Induction Control to Optimise Wind Farm Power under Varying Wind Directions P. Hulsman et al. https://doi.org/10.1088/1742-6596/3224/3/032115
74. Dynamic individual pitch control for wake mitigation: Why does the helix handedness in the wake matter? M. Coquelet et al. https://doi.org/10.1088/1742-6596/2767/9/092084
75. Winds of Progress: An In-Depth Exploration of Offshore, Floating, and Onshore Wind Turbines as Cornerstones for Sustainable Energy Generation and Environmental Stewardship S. Khan Afridi et al. https://doi.org/10.1109/ACCESS.2024.3397243
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79. Secondary flows in the actuator-disk simulation of wind-turbine wakes N. Zehtabiyan-Rezaie et al. https://doi.org/10.1063/5.0203068
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81. Optimal wind farm energy and reserve scheduling incorporating wake interactions M. Mabboux-Fort et al. https://doi.org/10.1016/j.apenergy.2026.127815
82. Model predictive control of wakes for wind farm power tracking A. Sterle et al. https://doi.org/10.1088/1742-6596/2767/3/032005
83. Risk-averse wake steering optimization for energy and power maximization under uncertain wind direction changes M. Becker & J. Wingerden https://doi.org/10.1088/1742-6596/3224/3/032124
84. Wind plant wake losses: Disconnect between turbine actuation and control of plant wakes with engineering wake models R. Scott et al. https://doi.org/10.1063/5.0207013
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86. Sensitivity analysis of computational domain height for semi-infinite and finite-sized wind farms W. Chanprasert et al. https://doi.org/10.1088/1742-6596/3016/1/012052
87. Field comparison of load-based wind turbine wake tracking with a scanning lidar reference D. Onnen et al. https://doi.org/10.5194/wes-11-175-2026
88. The Influence of Floating Turbine Dynamics on the Helix Wake Mixing Method D. Van Den Berg et al. https://doi.org/10.1088/1742-6596/2767/3/032012
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90. Evolution of eddy viscosity in the wake of a wind turbine R. Scott et al. https://doi.org/10.5194/wes-8-449-2023
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92. Exploring cooperation between wind farms: a wake steering optimization study of the Belgian offshore wind farm cluster B. Foloppe et al. https://doi.org/10.1088/1742-6596/2505/1/012055
93. Measuring HELIX active wake mixing using continuous wave light detection and ranging T. Crumpton et al. https://doi.org/10.1088/1742-6596/3224/3/032099
94. On the power and control of a misaligned rotor – beyond the cosine law S. Tamaro et al. https://doi.org/10.5194/wes-9-1547-2024
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