139 citations as recorded by crossref.

1. A physically interpretable data-driven surrogate model for wake steering B. Sengers et al. 10.5194/wes-7-1455-2022
2. Addressing deep array effects and impacts to wake steering with the cumulative-curl wake model C. Bay et al. 10.5194/wes-8-401-2023
3. Modelling and assessing the near-wake representation and turbulence behaviour of control-oriented wake models P. Hulsman et al. 10.1088/1742-6596/1618/2/022056
4. Towards fine tuning wake steering policies in the field: an imitation-based approach C. Bizon Monroc et al. 10.1088/1742-6596/2767/3/032017
5. The fluid mechanics of active flow control at very large scales C. Meneveau 10.1017/jfm.2024.846
6. Adaptation of Engineering Wake Models using Gaussian Process Regression and High-Fidelity Simulation Data L. Andersson et al. 10.1088/1742-6596/1618/2/022043
7. Experimental and numerical study of the wake deflections of scaled vertical axis wind turbine models M. Huang et al. 10.1088/1742-6596/2505/1/012019
8. Control-oriented model for secondary effects of wake steering J. King et al. 10.5194/wes-6-701-2021
9. Measuring wake deflection from SCADA data during wake steering using machine learning N. Post et al. 10.1088/1742-6596/2767/4/042031
10. Quantification of wake shape modulation and deflection for tilt and yaw misaligned wind turbines J. Bossuyt et al. 10.1017/jfm.2021.237
11. Highlighting the impact of yaw control by parsing atmospheric conditions based on total variation N. Hamilton 10.1088/1742-6596/1452/1/012006
12. Algorithms to harvest the wind D. Monroe 10.1145/3379497
13. Machine learning to rapidly predict turbine yaw angles for wake steering A. Stanley et al. 10.1088/1742-6596/2767/8/082011
14. Fast yaw optimization for wind plant wake steering using Boolean yaw angles A. Stanley et al. 10.5194/wes-7-741-2022
15. Distributed Fixed-Time Fatigue Minimization Control For Waked Wind Farms M. Firouzbahrami et al. 10.1109/TCST.2024.3362518
16. Blade planform design optimization to enhance turbine wake control J. Allen et al. 10.1002/we.2699
17. Investigating the impact of atmospheric conditions on wake-steering performance at a commercial wind plant E. Simley et al. 10.1088/1742-6596/2265/3/032097
18. A point vortex transportation model for yawed wind turbine wakes H. Zong & F. Porté-Agel 10.1017/jfm.2020.123
19. A vortex sheet based analytical model of the curled wake behind yawed wind turbines M. Bastankhah et al. 10.1017/jfm.2021.1010
20. How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine P. Murphy et al. 10.5194/wes-5-1169-2020
21. Wind Tunnel Testing of Yaw by Individual Pitch Control Applied to Wake Steering F. Campagnolo et al. 10.3389/fenrg.2022.883889
22. Dynamic wake field reconstruction of wind turbine through Physics-Informed Neural Network and Sparse LiDAR data L. Wang et al. 10.1016/j.energy.2024.130401
23. Wake steering of multirotor wind turbines G. Speakman et al. 10.1002/we.2633
24. Wind farm yaw control set-point optimization under model parameter uncertainty M. Howland 10.1063/5.0051071
25. Field Validation of Wake Steering Control with Wind Direction Variability E. Simley et al. 10.1088/1742-6596/1452/1/012012
26. Wake position tracking using dynamic wake meandering model and rotor loads L. Dong et al. 10.1063/5.0032917
27. Wind plant power maximization via extremum seeking yaw control: A wind tunnel experiment D. Kumar et al. 10.1002/we.2799
28. A quantitative review of wind farm control with the objective of wind farm power maximization A. Kheirabadi & R. Nagamune 10.1016/j.jweia.2019.06.015
29. Influence of atmospheric conditions on the power production of utility-scale wind turbines in yaw misalignment M. Howland et al. 10.1063/5.0023746
30. A Study of the Near Wake Deformation of the X‐Rotor Vertical‐Axis Wind Turbine With Pitched Blades D. Bensason et al. 10.1002/we.2944
31. Numerical Validation of Wind Plant Control Strategies S. Gomez-Iradi et al. 10.1088/1742-6596/1618/2/022010
32. On the power and control of a misaligned rotor – beyond the cosine law S. Tamaro et al. 10.5194/wes-9-1547-2024
33. Unified momentum model for rotor aerodynamics across operating regimes J. Liew et al. 10.1038/s41467-024-50756-5
34. Enabling control co-design of the next generation of wind power plants A. Stanley et al. 10.5194/wes-8-1341-2023
35. A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes M. Amiri et al. 10.1016/j.rser.2024.114279
36. Wind energy-harvesting technologies and recent research progresses in wind farm control models B. Desalegn et al. 10.3389/fenrg.2023.1124203
37. Real-time optimization of wind farms using modifier adaptation and machine learning L. Andersson & L. Imsland 10.5194/wes-5-885-2020
38. Experimental analysis of the effect of dynamic induction control on a wind turbine wake D. van der Hoek et al. 10.5194/wes-7-1305-2022
39. Exploring the complexities associated with full-scale wind plant wake mitigation control experiments J. Duncan Jr. et al. 10.5194/wes-5-469-2020
40. Experimental investigation and analytical modelling of active yaw control for wind farm power optimization H. Zong & F. Porté-Agel 10.1016/j.renene.2021.02.059
41. Characterizing tilt effects on wind plants R. Scott et al. 10.1063/5.0009853
42. Wind tunnel testing of wake steering with dynamic wind direction changes F. Campagnolo et al. 10.5194/wes-5-1273-2020
43. A yawed wake model to predict the velocity distribution of curled wake cross-section for wind turbines Q. Yang et al. 10.1016/j.oceaneng.2024.116911
44. Design and analysis of a wake steering controller with wind direction variability E. Simley et al. 10.5194/wes-5-451-2020
45. On the Robustness of Active Wake Control to Wind Turbine Downtime S. Kanev 10.3390/en12163152
46. Validation of induction/steering reserve-boosting active power control by a wind tunnel experiment with dynamic wind direction changes S. Tamaro et al. 10.1088/1742-6596/2767/9/092067
47. Hyperparameter tuning framework for calibrating analytical wake models using SCADA data of an offshore wind farm D. van Binsbergen et al. 10.5194/wes-9-1507-2024
48. Wake redirection for active power control: a realistic case study M. Kretschmer et al. 10.1088/1742-6596/1618/2/022059
49. Data-driven wake model parameter estimation to analyze effects of wake superposition M. LoCascio et al. 10.1063/5.0163896
50. Evaluation of the potential for wake steering for U.S. land-based wind power plants D. Bensason et al. 10.1063/5.0039325
51. Wind farm structural response and wake dynamics for an evolving stable boundary layer: computational and experimental comparisons K. Shaler et al. 10.5194/wes-9-1451-2024
52. How does the rotational direction of an upwind turbine affect its downwind neighbour? A. Englberger et al. 10.1088/1742-6596/2265/2/022048
53. Turbine power loss during yaw-misaligned free field tests at different atmospheric conditions P. Hulsman et al. 10.1088/1742-6596/2265/3/032074
54. Real-time identification of clusters of turbines F. Bernardoni et al. 10.1088/1742-6596/1618/2/022032
55. Further calibration and validation of FLORIS with wind tunnel data F. Campagnolo et al. 10.1088/1742-6596/2265/2/022019
56. Lifetime fatigue response due to wake steering on a pair of utility-scale wind turbines S. Dana et al. 10.1088/1742-6596/2265/2/022106
57. Analytical solutions for yawed wind-turbine wakes with application to wind-farm power optimization by active yaw control Z. Zhang et al. 10.1016/j.oceaneng.2024.117691
58. A CFD‐based analysis of dynamic induction techniques for wind farm control applications A. Croce et al. 10.1002/we.2801
59. U.S. East Coast Lidar Measurements Show Offshore Wind Turbines Will Encounter Very Low Atmospheric Turbulence N. Bodini et al. 10.1029/2019GL082636
60. On the load impact of dynamic wind farm wake mixing strategies J. Frederik & J. van Wingerden 10.1016/j.renene.2022.05.110
61. FarmConners wind farm flow control benchmark – Part 1: Blind test results T. Göçmen et al. 10.5194/wes-7-1791-2022
62. Closed-loop model-based wind farm control using FLORIS under time-varying inflow conditions B. Doekemeijer et al. 10.1016/j.renene.2020.04.007
63. Continued results from a field campaign of wake steering applied at a commercial wind farm – Part 2 P. Fleming et al. 10.5194/wes-5-945-2020
64. Control-oriented modelling of wind direction variability S. Dallas et al. 10.5194/wes-9-841-2024
65. Dynamic Flow Modelling for Model-Predictive Wind Farm Control M. van den Broek & J. Wingerden 10.1088/1742-6596/1618/2/022023
66. Grand challenges in the design, manufacture, and operation of future wind turbine systems P. Veers et al. 10.5194/wes-8-1071-2023
67. The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data R. Braunbehrens et al. 10.5194/wes-8-691-2023
68. Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance E. Simley et al. 10.5194/wes-6-1427-2021
69. A time‐varying formulation of the curled wake model within the FAST.Farm framework E. Branlard et al. 10.1002/we.2785
70. Development of a curled wake of a yawed wind turbine under turbulent and sheared inflow P. Hulsman et al. 10.5194/wes-7-237-2022
71. Comparison of the Gaussian Wind Farm Model with Historical Data of Three Offshore Wind Farms B. Doekemeijer et al. 10.3390/en15061964
72. Near-wake structure of full-scale vertical-axis wind turbines N. Wei et al. 10.1017/jfm.2020.578
73. Assessment of yaw-control effects on wind turbine-wake interaction: A coupled unsteady vortex lattice method and curled wake model analysis W. Han et al. 10.1016/j.jweia.2023.105559
74. A hybrid wake method for simulating yaw tandem wind turbine Y. Yuan et al. 10.1016/j.oceaneng.2024.119549
75. Field experiment for open-loop yaw-based wake steering at a commercial onshore wind farm in Italy B. Doekemeijer et al. 10.5194/wes-6-159-2021
76. A dynamic model of wind turbine yaw for active farm control G. Starke et al. 10.1002/we.2884
77. Assessing Closed-Loop Data-Driven Wind Farm Control Strategies within a Wind Tunnel P. Hulsman et al. 10.1088/1742-6596/2767/3/032049
78. Lidar measurements of yawed-wind-turbine wakes: characterization and validation of analytical models P. Brugger et al. 10.5194/wes-5-1253-2020
79. A Probabilistic Learning Approach Applied to the Optimization of Wake Steering in Wind Farms J. Almeida & F. Rochinha 10.1115/1.4054501
80. Increased power gains from wake steering control using preview wind direction information B. Sengers et al. 10.5194/wes-8-1693-2023
81. Koopman Model Predictive Control for Wind Farm Yield Optimization with Combined Thrust and Yaw Control A. Dittmer et al. 10.1016/j.ifacol.2023.10.1037
82. Wind farm power optimization through wake steering M. Howland et al. 10.1073/pnas.1903680116
83. Decreasing wind speed extrapolation error via domain-specific feature extraction and selection D. Vassallo et al. 10.5194/wes-5-959-2020
84. Can wind turbine farms increase settlement of particulate matters during dust events? M. Mataji et al. 10.1063/5.0129481
85. Experimental investigation of wind turbine wake and load dynamics during yaw maneuvers S. Macrí et al. 10.5194/wes-6-585-2021
86. Collective wind farm operation based on a predictive model increases utility-scale energy production M. Howland et al. 10.1038/s41560-022-01085-8
87. Measurement-driven large-eddy simulations of a diurnal cycle during a wake-steering field campaign E. Quon 10.5194/wes-9-495-2024
88. The Importance of Wake Meandering on Wind Turbine Fatigue Loads in Wake J. Rinker et al. 10.3390/en14217313
89. The Jensen wind farm parameterization Y. Ma et al. 10.5194/wes-7-2407-2022
90. Curled-Skewed Wakes behind Yawed Wind Turbines Subject to Veered Inflow M. Mohammadi et al. 10.3390/en15239135
91. Combining wake redirection and derating strategies in a load-constrained wind farm power maximization A. Croce et al. 10.5194/wes-9-1211-2024
92. A new method to characterize the curled wake shape under yaw misalignment B. Sengers et al. 10.1088/1742-6596/1618/6/062050
93. Wake steering optimization under uncertainty J. Quick et al. 10.5194/wes-5-413-2020
94. Wake redirection control for offshore wind farm power and fatigue multi-objective optimisation based on a wind turbine load indicator J. Sun et al. 10.1016/j.energy.2024.133893
95. Mechanisms of dynamic near-wake modulation of a utility-scale wind turbine A. Abraham et al. 10.1017/jfm.2021.737
96. Monte-Carlo simulations based hub height optimization using FLORIS for two interacting onshore wind farms G. Kütükçü & O. Uzol 10.1063/5.0107244
97. Wind farm flow control: prospects and challenges J. Meyers et al. 10.5194/wes-7-2271-2022
98. Data–Driven Wake Steering Control for a Simulated Wind Farm Model S. Simani et al. 10.31875/2409-9694.2023.10.02
99. Proof-of-concept of a reinforcement learning framework for wind farm energy capture maximization in time-varying wind P. Stanfel et al. 10.1063/5.0043091
100. Artificial intelligence-aided wind plant optimization for nationwide evaluation of land use and economic benefits of wake steering D. Harrison-Atlas et al. 10.1038/s41560-024-01516-8
101. Effect of Atmospheric Stability on Meandering and Wake Characteristics in Wind Turbine Fluid Dynamics B. Løvøy Alvestad et al. 10.3390/app14178025
102. Does the rotational direction of a wind turbine impact the wake in a stably stratified atmospheric boundary layer? A. Englberger et al. 10.5194/wes-5-1359-2020
103. Vertical wake deflection for floating wind turbines by differential ballast control E. Nanos et al. 10.5194/wes-7-1641-2022
104. Power increases using wind direction spatial filtering for wind farm control: Evaluation using FLORIS, modified for dynamic settings M. Sinner et al. 10.1063/5.0039899
105. Offshore Wind Turbines Will Encounter Very Low Atmospheric Turbulence N. Bodini et al. 10.1088/1742-6596/1452/1/012023
106. Impact of wake steering on loads of downstream wind turbines at an above-rated condition R. Thedin et al. 10.1088/1742-6596/2767/3/032020
107. Loads assessment of a fixed‐bottom offshore wind farm with wake steering K. Shaler et al. 10.1002/we.2756
108. Maximization of the Power Production of an Offshore Wind Farm R. Balakrishnan & S. Hur 10.3390/app12084013
109. On the wake deflection of vertical axis wind turbines by pitched blades M. Huang et al. 10.1002/we.2803
110. Active Wake Steering Control Data-Driven Design for a Wind Farm Benchmark S. Simani et al. 10.1016/j.ifacol.2023.10.1504
111. 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
112. Are steady-state wake models and lookup tables sufficient to design profitable wake steering strategies? A Large Eddy Simulation investigation M. Lejeune et al. 10.1088/1742-6596/2767/9/092075
113. Combined wake control of aligned wind turbines for power optimization based on a 3D wake model considering secondary wake steering Y. Liu et al. 10.1016/j.energy.2024.132900
114. Wind farm power maximization through wake steering with a new multiple wake model for prediction of turbulence intensity G. Qian & T. Ishihara 10.1016/j.energy.2020.119680
115. A LiDAR-Based Active Yaw Control Strategy for Optimal Wake Steering in Paired Wind Turbines E. Mahmoodi et al. 10.3390/en17225635
116. Development and validation of a hybrid data-driven model-based wake steering controller and its application at a utility-scale wind plant P. Bachant et al. 10.5194/wes-9-2235-2024
117. Optimal closed-loop wake steering – Part 2: Diurnal cycle atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-7-345-2022
118. Digital twin of wind farms via physics-informed deep learning J. Zhang & X. Zhao 10.1016/j.enconman.2023.117507
119. Effects of wind veer on a yawed wind turbine wake in atmospheric boundary layer flow G. Narasimhan et al. 10.1103/PhysRevFluids.7.114609
120. Scientific challenges to characterizing the wind resource in the marine atmospheric boundary layer W. Shaw et al. 10.5194/wes-7-2307-2022
121. Optimal closed-loop wake steering – Part 1: Conventionally neutral atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-5-1315-2020
122. Changing the rotational direction of a wind turbine under veering inflow: a parameter study A. Englberger et al. 10.5194/wes-5-1623-2020
123. Identification of wind turbine clusters for effective real time yaw control optimization F. Bernardoni et al. 10.1063/5.0036640
124. Influence of Wake Model Superposition and Secondary Steering on Model-Based Wake Steering Control with SCADA Data Assimilation M. Howland & J. Dabiri 10.3390/en14010052
125. Wind farm control ‐ Part I: A review on control system concepts and structures L. Andersson et al. 10.1049/rpg2.12160
126. Turbulence and Control of Wind Farms C. Shapiro et al. 10.1146/annurev-control-070221-114032
127. Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment B. Sengers et al. 10.5194/wes-8-747-2023
128. Observability of the ambient conditions in model‐based estimation for wind farm control: A focus on static models B. Doekemeijer & J. van Wingerden 10.1002/we.2495
129. Experimental results of wake steering using fixed angles P. Fleming et al. 10.5194/wes-6-1521-2021
130. Expert Elicitation on Wind Farm Control J. van Wingerden et al. 10.1088/1742-6596/1618/2/022025
131. The curled wake model: a three-dimensional and extremely fast steady-state wake solver for wind plant flows L. Martínez-Tossas et al. 10.5194/wes-6-555-2021
132. Review of wake management techniques for wind turbines D. Houck 10.1002/we.2668
133. Wind Farm Loads under Wake Redirection Control S. Kanev et al. 10.3390/en13164088
134. Modelling the induction, thrust and power of a yaw-misaligned actuator disk K. Heck et al. 10.1017/jfm.2023.129
135. How generalizable is a machine-learning approach for modeling hub-height turbulence intensity? N. Bodini et al. 10.1088/1742-6596/2265/2/022028
136. Data-driven optimisation of wind farm layout and wake steering with large-eddy simulations N. Bempedelis et al. 10.5194/wes-9-869-2024
137. Design and analysis of a wake model for spatially heterogeneous flow A. Farrell et al. 10.5194/wes-6-737-2021
138. Method to predict the minimum measurement and experiment durations needed to achieve converged and significant results in a wind energy field experiment D. Houck et al. 10.5194/wes-9-1189-2024
139. Wind Farm Modeling with Interpretable Physics-Informed Machine Learning M. Howland & J. Dabiri 10.3390/en12142716