78 citations as recorded by crossref.

1. The curled wake model: a three-dimensional and extremely fast steady-state wake solver for wind plant flows L. Martínez-Tossas et al. https://doi.org/10.5194/wes-6-555-2021
2. Grand challenges in the design, manufacture, and operation of future wind turbine systems P. Veers et al. https://doi.org/10.5194/wes-8-1071-2023
3. A novel hybrid model for the estimation of energy conversion in a wind farm combining wake effects and stochastic dependability F. Famoso et al. https://doi.org/10.1016/j.apenergy.2020.115967
4. Evaluation of the potential for wake steering for U.S. land-based wind power plants D. Bensason et al. https://doi.org/10.1063/5.0039325
5. Multi-strategy wind farm control: alternating wake steering and helix wake mixing on a large-scale wind farm M. Baricchio et al. https://doi.org/10.5194/wes-11-2817-2026
6. A Probabilistic Learning Approach Applied to the Optimization of Wake Steering in Wind Farms J. Almeida & F. Rochinha https://doi.org/10.1115/1.4054501
7. Advancing wind turbines through control co-design: An integrative review S. Bayat et al. https://doi.org/10.1016/j.apenergy.2026.127951
8. 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
9. Analysis of horizontal wind direction variability considering different influencing factors Z. Shu et al. https://doi.org/10.1016/j.jweia.2024.105819
10. Control-oriented modelling of wind direction variability S. Dallas et al. https://doi.org/10.5194/wes-9-841-2024
11. A Tutorial on the Control of Floating Offshore Wind Turbines: Stability Challenges and Opportunities for Power Capture D. Stockhouse et al. https://doi.org/10.1109/MCS.2024.3433208
12. Power Enhancement of a Wind Farm Using Moth-Flame Optimization-based Wake Steering Control D. Cheon et al. https://doi.org/10.1088/1742-6596/3224/3/032029
13. PAM-Net: Offshore vector wind field prediction based on hierarchical frequency quantization and spatiotemporal synergistic Mamba network J. Feng et al. https://doi.org/10.1016/j.epsr.2026.113455
14. Monte-Carlo simulations based hub height optimization using FLORIS for two interacting onshore wind farms G. Kütükçü & O. Uzol https://doi.org/10.1063/5.0107244
15. Investigating the impact of atmospheric conditions on wake-steering performance at a commercial wind plant E. Simley et al. https://doi.org/10.1088/1742-6596/2265/3/032097
16. The Jensen wind farm parameterization Y. Ma et al. https://doi.org/10.5194/wes-7-2407-2022
17. Study on the yaw-based wake steering control considering dynamic flow characteristics for wind farm power improvement X. Yu et al. https://doi.org/10.1088/1742-6596/2505/1/012010
18. Comparison of wind-farm control strategies under realistic offshore wind conditions: wake quantities of interest K. Brown et al. https://doi.org/10.5194/wes-10-1737-2025
19. Improving Floating Offshore Wind Farm Flow Control With Scalable Model-Based Deep Reinforcement Learning M. Mei et al. https://doi.org/10.1109/TASE.2025.3585016
20. Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance E. Simley et al. https://doi.org/10.5194/wes-6-1427-2021
21. Artificial intelligence-aided wind plant optimization for nationwide evaluation of land use and economic benefits of wake steering D. Harrison-Atlas et al. https://doi.org/10.1038/s41560-024-01516-8
22. Deep reinforcement learning-driven wind farm flow control considering dynamic wind H. Wang et al. https://doi.org/10.1016/j.enconman.2025.119888
23. Increased power gains from wake steering control using preview wind direction information B. Sengers et al. https://doi.org/10.5194/wes-8-1693-2023
24. 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
25. Dynamic wake tracking based on wind turbine rotor loads and Kalman filtering D. Onnen et al. https://doi.org/10.1088/1742-6596/2265/2/022024
26. Data-driven yaw misalignment correction for utility-scale wind turbines L. Gao & J. Hong https://doi.org/10.1063/5.0056671
27. On Wind Directions Estimated by Nacelle Lidar Under Different Reconstruction Methods F. Guo et al. https://doi.org/10.1002/we.70098
28. Design, Implementation and Software-in-the-Loop Validation of an Online Wake-Steering Control System P. Tona et al. https://doi.org/10.1088/1742-6596/3224/3/032141
29. Wind farm yaw control set-point optimization under model parameter uncertainty M. Howland https://doi.org/10.1063/5.0051071
30. 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
31. Power increases using wind direction spatial filtering for wind farm control: Evaluation using FLORIS, modified for dynamic settings M. Sinner et al. https://doi.org/10.1063/5.0039899
32. Numerical investigation of rotor asymmetry to promote wake recovery A. Abraham et al. https://doi.org/10.1088/1742-6596/2505/1/012032
33. Wind tunnel testing of wake steering with dynamic wind direction changes F. Campagnolo et al. https://doi.org/10.5194/wes-5-1273-2020
34. A Data-Driven Observer for Wind Farm Power Gain Potential: A Sparse Koopman Operator Approach Y. Chen et al. https://doi.org/10.3390/en18143751
35. Rotating mesh framework and dynamic wind direction variability effects on building ventilation with large eddy simulation H. Zhu et al. https://doi.org/10.1063/5.0267647
36. Turbulence and Control of Wind Farms C. Shapiro et al. https://doi.org/10.1146/annurev-control-070221-114032
37. FarmConners market showcase results: wind farm flow control considering electricity prices K. Kölle et al. https://doi.org/10.5194/wes-7-2181-2022
38. Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment B. Sengers et al. https://doi.org/10.5194/wes-8-747-2023
39. Reinforcement learning for wind-farm flow control: Current state and future actions M. Abkar et al. https://doi.org/10.1016/j.taml.2023.100475
40. Proof of concept for multirotor systems with vortex-generating modes for regenerative wind energy: a study based on numerical simulations and experimental data F. Avila Correia Martins et al. https://doi.org/10.5194/wes-10-41-2025
41. Continued results from a field campaign of wake steering applied at a commercial wind farm – Part 2 P. Fleming et al. https://doi.org/10.5194/wes-5-945-2020
42. An integrated multi-timescale MPC framework for coordinated wind farm power maximization, load management, and grid support B. Wang et al. https://doi.org/10.1016/j.renene.2025.123903
43. 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
44. FarmConners wind farm flow control benchmark – Part 1: Blind test results T. Göçmen et al. https://doi.org/10.5194/wes-7-1791-2022
45. An efficient optimization algorithm for active yaw control to increase wind farm power while considering load reduction Y. Wang et al. https://doi.org/10.1016/j.energy.2025.139116
46. Wind-farm power prediction using a turbulence-optimized Gaussian wake model N. Zehtabiyan-Rezaie et al. https://doi.org/10.1016/j.weer.2024.100007
47. Control-oriented model for secondary effects of wake steering J. King et al. https://doi.org/10.5194/wes-6-701-2021
48. Predicting the benefit of wake steering on the annual energy production of a wind farm using large eddy simulations and Gaussian process regression D. Hoek et al. https://doi.org/10.1088/1742-6596/1618/2/022024
49. Wake position tracking using dynamic wake meandering model and rotor loads L. Dong et al. https://doi.org/10.1063/5.0032917
50. 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
51. Time-domain fatigue damage assessment for wind turbine tower bolts under yaw optimization control at offshore wind farm T. Tao et al. https://doi.org/10.1016/j.oceaneng.2024.117706
52. Fast yaw optimization for wind plant wake steering using Boolean yaw angles A. Stanley et al. https://doi.org/10.5194/wes-7-741-2022
53. Data-driven wake model parameter estimation to analyze effects of wake superposition M. LoCascio et al. https://doi.org/10.1063/5.0163896
54. A machine learning-based fatigue loads and power prediction method for wind turbines under yaw control R. He et al. https://doi.org/10.1016/j.apenergy.2022.120013
55. 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
56. Experimental results of wake steering using fixed angles P. Fleming et al. https://doi.org/10.5194/wes-6-1521-2021
57. Discovering low-dimensional embedding equations and reconstruction from sparse data via latent differential spectral operator Q. Feng et al. https://doi.org/10.1016/j.jcp.2026.115391
58. Wind energy-harvesting technologies and recent research progresses in wind farm control models B. Desalegn et al. https://doi.org/10.3389/fenrg.2023.1124203
59. Sensitivity analysis of wake steering optimisation for wind farm power maximisation F. Gori et al. https://doi.org/10.5194/wes-8-1425-2023
60. Can wind turbine farms increase settlement of particulate matters during dust events? M. Mataji et al. https://doi.org/10.1063/5.0129481
61. A physically interpretable data-driven surrogate model for wake steering B. Sengers et al. https://doi.org/10.5194/wes-7-1455-2022
62. Wind farm control ‐ Part I: A review on control system concepts and structures L. Andersson et al. https://doi.org/10.1049/rpg2.12160
63. 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
64. Optimal closed-loop wake steering – Part 2: Diurnal cycle atmospheric boundary layer conditions M. Howland et al. https://doi.org/10.5194/wes-7-345-2022
65. Error analysis of low-fidelity models for wake steering based on field measurements S. Letizia et al. https://doi.org/10.1088/1742-6596/2767/4/042029
66. Modeling dynamic wind direction changes in large eddy simulations of wind farms A. Stieren et al. https://doi.org/10.1016/j.renene.2021.02.018
67. Reductions in wind farm main bearing rating lives resulting from wake impingement J. Quick et al. https://doi.org/10.5194/wes-11-493-2026
68. Influence of Wake Model Superposition and Secondary Steering on Model-Based Wake Steering Control with SCADA Data Assimilation M. Howland & J. Dabiri https://doi.org/10.3390/en14010052
69. Stochastic Model Predictive Control With Gaussian Wind Direction Preview for Wake Steering A. Henry et al. https://doi.org/10.1109/TCST.2025.3639652
70. Experimental analysis of co-rotating and counter-rotating tandem horizontal-axis wind turbine performance and wake dynamics P. Bayron et al. https://doi.org/10.1016/j.jweia.2024.105840
71. Wind direction variability and simplified modeling of multiple hazardous extreme wind events from field measurements S. Wang et al. https://doi.org/10.1016/j.buildenv.2026.114665
72. Lidar measurements of yawed-wind-turbine wakes: characterization and validation of analytical models P. Brugger et al. https://doi.org/10.5194/wes-5-1253-2020
73. Assessing Closed-Loop Data-Driven Wind Farm Control Strategies within a Wind Tunnel P. Hulsman et al. https://doi.org/10.1088/1742-6596/2767/3/032049
74. Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm M. van Beek et al. https://doi.org/10.3390/en14051293
75. An improved wake steering optimization method for enhanced wind energy production using realistic yaw behaviour Z. Xie & Z. Lin https://doi.org/10.1016/j.renene.2026.126160
76. Control Co-Design of Wind Turbines L. Pao et al. https://doi.org/10.1146/annurev-control-061423-101708
77. Wind tunnel study of yawed porous discs subjected to veered inflow S. Purohit et al. https://doi.org/10.5194/wes-11-3671-2026
78. A dynamic open-source model to investigate wake dynamics in response to wind farm flow control strategies M. Becker et al. https://doi.org/10.5194/wes-10-1055-2025