168 citations as recorded by crossref.

1. A physically interpretable data-driven surrogate model for wake steering B. Sengers et al.
2. Addressing deep array effects and impacts to wake steering with the cumulative-curl wake model C. Bay et al.
3. Modelling and assessing the near-wake representation and turbulence behaviour of control-oriented wake models P. Hulsman et al.
4. Towards fine tuning wake steering policies in the field: an imitation-based approach C. Bizon Monroc et al.
5. The fluid mechanics of active flow control at very large scales C. Meneveau
6. Research on Wake Characteristics of Dynamic Yawing Offshore Wind Turbine by Proper Orthogonal Decomposition O. Sabbar et al.
7. Adaptation of Engineering Wake Models using Gaussian Process Regression and High-Fidelity Simulation Data L. Andersson et al.
8. Experimental and numerical study of the wake deflections of scaled vertical axis wind turbine models M. Huang et al.
9. Control-oriented model for secondary effects of wake steering J. King et al.
10. Measuring wake deflection from SCADA data during wake steering using machine learning N. Post et al.
11. Quantification of wake shape modulation and deflection for tilt and yaw misaligned wind turbines J. Bossuyt et al.
12. Highlighting the impact of yaw control by parsing atmospheric conditions based on total variation N. Hamilton
13. Algorithms to harvest the wind D. Monroe
14. Machine learning to rapidly predict turbine yaw angles for wake steering A. Stanley et al.
15. Load assessment of a wind farm considering negative and positive yaw misalignment for wake steering R. Thedin et al.
16. Observations of wind farm wake recovery at an operating wind farm R. Krishnamurthy et al.
17. Fast yaw optimization for wind plant wake steering using Boolean yaw angles A. Stanley et al.
18. Distributed Fixed-Time Fatigue Minimization Control For Waked Wind Farms M. Firouzbahrami et al.
19. Blade planform design optimization to enhance turbine wake control J. Allen et al.
20. Investigating the impact of atmospheric conditions on wake-steering performance at a commercial wind plant E. Simley et al.
21. A point vortex transportation model for yawed wind turbine wakes H. Zong & F. Porté-Agel
22. A vortex sheet based analytical model of the curled wake behind yawed wind turbines M. Bastankhah et al.
23. How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine P. Murphy et al.
24. Wind Tunnel Testing of Yaw by Individual Pitch Control Applied to Wake Steering F. Campagnolo et al.
25. Dynamic wake field reconstruction of wind turbine through Physics-Informed Neural Network and Sparse LiDAR data L. Wang et al.
26. Wake field prediction of a wind farm based on a physics-informed neural network with different spatiotemporal prediction performance improvement strategies J. Song et al.
27. Wake steering of multirotor wind turbines G. Speakman et al.
28. Wind farm yaw control set-point optimization under model parameter uncertainty M. Howland
29. Wind Farm Control via Offline Reinforcement Learning With Adversarial Training Y. Huang & X. Zhao
30. Field Validation of Wake Steering Control with Wind Direction Variability E. Simley et al.
31. Comparison of wind-farm control strategies under realistic offshore wind conditions: wake quantities of interest K. Brown et al.
32. Wake position tracking using dynamic wake meandering model and rotor loads L. Dong et al.
33. Wind plant power maximization via extremum seeking yaw control: A wind tunnel experiment D. Kumar et al.
34. A quantitative review of wind farm control with the objective of wind farm power maximization A. Kheirabadi & R. Nagamune
35. Influence of atmospheric conditions on the power production of utility-scale wind turbines in yaw misalignment M. Howland et al.
36. A Study of the Near Wake Deformation of the X‐Rotor Vertical‐Axis Wind Turbine With Pitched Blades D. Bensason et al.
37. Numerical Validation of Wind Plant Control Strategies S. Gomez-Iradi et al.
38. On the power and control of a misaligned rotor – beyond the cosine law S. Tamaro et al.
39. Unified momentum model for rotor aerodynamics across operating regimes J. Liew et al.
40. Enabling control co-design of the next generation of wind power plants A. Stanley et al.
41. An extended analytical wake model and applications to yawed wind turbines in atmospheric boundary layers with different levels of stratification and veer G. Narasimhan et al.
42. A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes M. Amiri et al.
43. Wind energy-harvesting technologies and recent research progresses in wind farm control models B. Desalegn et al.
44. Real-time optimization of wind farms using modifier adaptation and machine learning L. Andersson & L. Imsland
45. Data-driven wind farm flow control and challenges towards field implementation: A review T. Göçmen et al.
46. Experimental analysis of the effect of dynamic induction control on a wind turbine wake D. van der Hoek et al.
47. Exploring the complexities associated with full-scale wind plant wake mitigation control experiments J. Duncan Jr. et al.
48. Experimental investigation and analytical modelling of active yaw control for wind farm power optimization H. Zong & F. Porté-Agel
49. Characterizing tilt effects on wind plants R. Scott et al.
50. Wind tunnel testing of wake steering with dynamic wind direction changes F. Campagnolo et al.
51. On Wind Directions Estimated by Nacelle Lidar Under Different Reconstruction Methods F. Guo et al.
52. A yawed wake model to predict the velocity distribution of curled wake cross-section for wind turbines Q. Yang et al.
53. On the wake re-energization of the X-Rotor vertical-axis wind turbine via the vortex-generator strategy D. Bensason et al.
54. Design and analysis of a wake steering controller with wind direction variability E. Simley et al.
55. On the Robustness of Active Wake Control to Wind Turbine Downtime S. Kanev
56. Validation of induction/steering reserve-boosting active power control by a wind tunnel experiment with dynamic wind direction changes S. Tamaro et al.
57. Hyperparameter tuning framework for calibrating analytical wake models using SCADA data of an offshore wind farm D. van Binsbergen et al.
58. Wake redirection for active power control: a realistic case study M. Kretschmer et al.
59. Data-driven wake model parameter estimation to analyze effects of wake superposition M. LoCascio et al.
60. Comparison of wind farm control strategies under realistic offshore wind conditions: turbine quantities of interest J. Frederik et al.
61. Are vertical-axis wind turbines a scientific mirage or the future of offshore wind energy? A critical perspective a century after Darrieus’ patent A. Bianchini et al.
62. Evaluation of the potential for wake steering for U.S. land-based wind power plants D. Bensason et al.
63. Wind farm structural response and wake dynamics for an evolving stable boundary layer: computational and experimental comparisons K. Shaler et al.
64. How does the rotational direction of an upwind turbine affect its downwind neighbour? A. Englberger et al.
65. Turbine power loss during yaw-misaligned free field tests at different atmospheric conditions P. Hulsman et al.
66. Bidirectional wakes over complex terrain using SCADA data and wake models N. Sasanuma et al.
67. Real-time identification of clusters of turbines F. Bernardoni et al.
68. Further calibration and validation of FLORIS with wind tunnel data F. Campagnolo et al.
69. Lifetime fatigue response due to wake steering on a pair of utility-scale wind turbines S. Dana et al.
70. Analytical solutions for yawed wind-turbine wakes with application to wind-farm power optimization by active yaw control Z. Zhang et al.
71. A CFD‐based analysis of dynamic induction techniques for wind farm control applications A. Croce et al.
72. U.S. East Coast Lidar Measurements Show Offshore Wind Turbines Will Encounter Very Low Atmospheric Turbulence N. Bodini et al.
73. On the load impact of dynamic wind farm wake mixing strategies J. Frederik & J. van Wingerden
74. FarmConners wind farm flow control benchmark – Part 1: Blind test results T. Göçmen et al.
75. Closed-loop model-based wind farm control using FLORIS under time-varying inflow conditions B. Doekemeijer et al.
76. Continued results from a field campaign of wake steering applied at a commercial wind farm – Part 2 P. Fleming et al.
77. Control-oriented modelling of wind direction variability S. Dallas et al.
78. Advancing wind turbines through control co-design: An integrative review S. Bayat et al.
79. Dynamic Flow Modelling for Model-Predictive Wind Farm Control M. van den Broek & J. Wingerden
80. Grand challenges in the design, manufacture, and operation of future wind turbine systems P. Veers et al.
81. Characterizing Wake Behavior of Adaptive Aerodynamic Structures Using Reduced-Order Models K. Sadeghilari et al.
82. The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data R. Braunbehrens et al.
83. Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance E. Simley et al.
84. A time‐varying formulation of the curled wake model within the FAST.Farm framework E. Branlard et al.
85. Development of a curled wake of a yawed wind turbine under turbulent and sheared inflow P. Hulsman et al.
86. Comparison of the Gaussian Wind Farm Model with Historical Data of Three Offshore Wind Farms B. Doekemeijer et al.
87. Near-wake structure of full-scale vertical-axis wind turbines N. Wei et al.
88. 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.
89. A hybrid wake method for simulating yaw tandem wind turbine Y. Yuan et al.
90. A robust active power control algorithm to maximize wind farm power tracking margins in waked conditions S. Tamaro et al.
91. Field experiment for open-loop yaw-based wake steering at a commercial onshore wind farm in Italy B. Doekemeijer et al.
92. A dynamic model of wind turbine yaw for active farm control G. Starke et al.
93. Assessing Closed-Loop Data-Driven Wind Farm Control Strategies within a Wind Tunnel P. Hulsman et al.
94. Lidar measurements of yawed-wind-turbine wakes: characterization and validation of analytical models P. Brugger et al.
95. Positive effect of wind farm control on energy production and revenue in European markets F. Wasilczuk et al.
96. A Probabilistic Learning Approach Applied to the Optimization of Wake Steering in Wind Farms J. Almeida & F. Rochinha
97. Increased power gains from wake steering control using preview wind direction information B. Sengers et al.
98. Koopman Model Predictive Control for Wind Farm Yield Optimization with Combined Thrust and Yaw Control A. Dittmer et al.
99. Wind farm power optimization through wake steering M. Howland et al.
100. Decreasing wind speed extrapolation error via domain-specific feature extraction and selection D. Vassallo et al.
101. LiDAR-assisted closed-loop control of a wind farm D. Dubuc & P. Tona
102. Can wind turbine farms increase settlement of particulate matters during dust events? M. Mataji et al.
103. Experimental investigation of wind turbine wake and load dynamics during yaw maneuvers S. Macrí et al.
104. Collective wind farm operation based on a predictive model increases utility-scale energy production M. Howland et al.
105. Trailing-Edge Noise and Amplitude Modulation Under Yaw-Induced Partial Wake: A Curl–UVLM Analysis with Atmospheric Stability Effects H. Kim et al.
106. Measurement-driven large-eddy simulations of a diurnal cycle during a wake-steering field campaign E. Quon
107. 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.
108. The Importance of Wake Meandering on Wind Turbine Fatigue Loads in Wake J. Rinker et al.
109. The Jensen wind farm parameterization Y. Ma et al.
110. Curled-Skewed Wakes behind Yawed Wind Turbines Subject to Veered Inflow M. Mohammadi et al.
111. Combining wake redirection and derating strategies in a load-constrained wind farm power maximization A. Croce et al.
112. A new method to characterize the curled wake shape under yaw misalignment B. Sengers et al.
113. Wake steering optimization under uncertainty J. Quick et al.
114. Wake redirection control for offshore wind farm power and fatigue multi-objective optimisation based on a wind turbine load indicator J. Sun et al.
115. A Supervisory Control Framework for Fatigue-Aware Wake Steering in Wind Farms Y. Shen et al.
116. Comparative Study on Wake Characteristics of Dual-Rotor Wind Turbines under Varying Thrust Coefficient and Turbulence Intensity H. Kim et al.
117. Mechanisms of dynamic near-wake modulation of a utility-scale wind turbine A. Abraham et al.
118. Monte-Carlo simulations based hub height optimization using FLORIS for two interacting onshore wind farms G. Kütükçü & O. Uzol
119. Wind farm flow control: prospects and challenges J. Meyers et al.
120. Data–Driven Wake Steering Control for a Simulated Wind Farm Model S. Simani et al.
121. Proof-of-concept of a reinforcement learning framework for wind farm energy capture maximization in time-varying wind P. Stanfel et al.
122. Fatigue analysis of wind turbine and load reduction through wind-farm-level yaw control Y. Wang & Z. Liu
123. Artificial intelligence-aided wind plant optimization for nationwide evaluation of land use and economic benefits of wake steering D. Harrison-Atlas et al.
124. Effect of Atmospheric Stability on Meandering and Wake Characteristics in Wind Turbine Fluid Dynamics B. Løvøy Alvestad et al.
125. Does the rotational direction of a wind turbine impact the wake in a stably stratified atmospheric boundary layer? A. Englberger et al.
126. Vertical wake deflection for floating wind turbines by differential ballast control E. Nanos et al.
127. Power increases using wind direction spatial filtering for wind farm control: Evaluation using FLORIS, modified for dynamic settings M. Sinner et al.
128. Offshore Wind Turbines Will Encounter Very Low Atmospheric Turbulence N. Bodini et al.
129. Impact of wake steering on loads of downstream wind turbines at an above-rated condition R. Thedin et al.
130. A Data-Driven Observer for Wind Farm Power Gain Potential: A Sparse Koopman Operator Approach Y. Chen et al.
131. Loads assessment of a fixed‐bottom offshore wind farm with wake steering K. Shaler et al.
132. Maximization of the Power Production of an Offshore Wind Farm R. Balakrishnan & S. Hur
133. On the wake deflection of vertical axis wind turbines by pitched blades M. Huang et al.
134. Active Wake Steering Control Data-Driven Design for a Wind Farm Benchmark S. Simani et al.
135. Evaluation of the impact of active wake control techniques on ultimate loads for a 10 MW wind turbine A. Croce et al.
136. Are steady-state wake models and lookup tables sufficient to design profitable wake steering strategies? A Large Eddy Simulation investigation M. Lejeune et al.
137. Combined wake control of aligned wind turbines for power optimization based on a 3D wake model considering secondary wake steering Y. Liu et al.
138. Wind farm power maximization through wake steering with a new multiple wake model for prediction of turbulence intensity G. Qian & T. Ishihara
139. A LiDAR-Based Active Yaw Control Strategy for Optimal Wake Steering in Paired Wind Turbines E. Mahmoodi et al.
140. 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.
141. Optimal closed-loop wake steering – Part 2: Diurnal cycle atmospheric boundary layer conditions M. Howland et al.
142. Wake steering in offshore wind farms: Field experiment and SCADA-driven simulations for quantifying power and blade root loads H. Wang et al.
143. Digital twin of wind farms via physics-informed deep learning J. Zhang & X. Zhao
144. LiDAR-Referenced Inflow Wind Condition Estimation from SCADA Data Using a Deep Learning Model S. He et al.
145. Effects of wind veer on a yawed wind turbine wake in atmospheric boundary layer flow G. Narasimhan et al.
146. Scientific challenges to characterizing the wind resource in the marine atmospheric boundary layer W. Shaw et al.
147. Optimal closed-loop wake steering – Part 1: Conventionally neutral atmospheric boundary layer conditions M. Howland et al.
148. Changing the rotational direction of a wind turbine under veering inflow: a parameter study A. Englberger et al.
149. Identification of wind turbine clusters for effective real time yaw control optimization F. Bernardoni et al.
150. Maximizing Wind Farm Power Output through Site-Specific Wake Model Calibration and Yaw Optimization Y. Liu et al.
151. Influence of Wake Model Superposition and Secondary Steering on Model-Based Wake Steering Control with SCADA Data Assimilation M. Howland & J. Dabiri
152. Wind farm control ‐ Part I: A review on control system concepts and structures L. Andersson et al.
153. Turbulence and Control of Wind Farms C. Shapiro et al.
154. Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment B. Sengers et al.
155. Observability of the ambient conditions in model‐based estimation for wind farm control: A focus on static models B. Doekemeijer & J. van Wingerden
156. Experimental results of wake steering using fixed angles P. Fleming et al.
157. Statistical Characteristics and Spatial Distributions of Aerodynamic Loads on Offshore Fixed-Bottom Wind Turbines under Wake Interference Y. Ren et al.
158. Expert Elicitation on Wind Farm Control J. van Wingerden et al.
159. The curled wake model: a three-dimensional and extremely fast steady-state wake solver for wind plant flows L. Martínez-Tossas et al.
160. Review of wake management techniques for wind turbines D. Houck
161. Wind Farm Loads under Wake Redirection Control S. Kanev et al.
162. Investigations into deep Reinforcement Learning for wind farm set-point optimisation H. Sheehan et al.
163. Spatio-temporal graph neural networks for power prediction in offshore wind farms using SCADA data S. Daenens et al.
164. Modelling the induction, thrust and power of a yaw-misaligned actuator disk K. Heck et al.
165. How generalizable is a machine-learning approach for modeling hub-height turbulence intensity? N. Bodini et al.
166. Data-driven optimisation of wind farm layout and wake steering with large-eddy simulations N. Bempedelis et al.
167. Design and analysis of a wake model for spatially heterogeneous flow A. Farrell et al.
168. 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.