78 citations as recorded by crossref.

1. Stochastic Dynamical Modeling of Wind Farm Turbulence A. Bhatt et al. 10.3390/en16196908
2. Free-vortex models for wind turbine wakes under yaw misalignment – a validation study on far-wake effects M. van den Broek et al. 10.5194/wes-8-1909-2023
3. A new analytical wind turbine wake model considering the effects of coriolis force and yawed conditions R. Snaiki & S. Makki 10.1016/j.jweia.2024.105767
4. Multi-objective Bayesian optimisation over sparse subspaces for model predictive control of wind farms K. Hoang et al. 10.1016/j.renene.2025.122988
5. Wind farm flow control: prospects and challenges J. Meyers et al. 10.5194/wes-7-2271-2022
6. Composite Experience Replay-Based Deep Reinforcement Learning With Application in Wind Farm Control H. Dong & X. Zhao 10.1109/TCST.2021.3102476
7. The restricted nonlinear large eddy simulation approach to reduced-order wind farm modeling J. Bretheim et al. 10.1063/1.5026325
8. Optimal combined wake and active power control of large‐scale wind farm considering available power W. Chen et al. 10.1049/rpg2.12883
9. Wind farm control part II: Model requirements to design and test wind farm flow control A. Stock et al. 10.1049/rpg2.13149
10. Improving wind farm flow models by learning from operational data J. Schreiber et al. 10.5194/wes-5-647-2020
11. Model predictive control strategy in waked wind farms for optimal fatigue loads C. Zhong et al. 10.1016/j.epsr.2023.109793
12. A multi-objective predictive control strategy for enhancing primary frequency support with wind farms S. Siniscalchi-Minna et al. 10.1088/1742-6596/1037/3/032034
13. Model-Optimized Dispatch for Closed-Loop Power Control of Waked Wind Farms J. Kazda & N. Cutululis 10.1109/TCST.2019.2923779
14. A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes M. Amiri et al. 10.1016/j.rser.2024.114279
15. Dual-Stage MPC-Based AGC for Wind Farm Considering Aerodynamic Interactions Z. Huang & W. Wu 10.1109/TSTE.2024.3502518
16. Wind farm supervisory controller design for power optimization in localized areas using adaptive learning game theory (ALGT) V. Fazlollahi et al. 10.1177/0309524X231199432
17. Maximization of the Power Production of an Offshore Wind Farm R. Balakrishnan & S. Hur 10.3390/app12084013
18. Comparative Analysis of Wind Farm Simulators for Wind Farm Control M. Kim et al. 10.3390/en16093676
19. Reinforcement Learning-Based Wind Farm Control: Toward Large Farm Applications via Automatic Grouping and Transfer Learning H. Dong & X. Zhao 10.1109/TII.2023.3252540
20. LES Study of Wake Meandering in Different Atmospheric Stabilities and Its Effects on Wind Turbine Aerodynamics X. Ning & D. Wan 10.3390/su11246939
21. MPC-Based Fatigue Load Suppression of Waked Wind Farm With 2Dof WT Control Strategy W. Chen et al. 10.1109/TSTE.2024.3407775
22. A constrained wind farm controller providing secondary frequency regulation: An LES study S. Boersma et al. 10.1016/j.renene.2018.11.031
23. Dynamic Flow Modelling for Model-Predictive Wind Farm Control M. van den Broek & J. Wingerden 10.1088/1742-6596/1618/2/022023
24. The area localized coupled model for analytical mean flow prediction in arbitrary wind farm geometries G. Starke et al. 10.1063/5.0042573
25. Influence of atmospheric stability on wind farm performance in complex terrain W. Radünz et al. 10.1016/j.apenergy.2020.116149
26. Model Predictive Active Power Control for Optimal Structural Load Equalization in Waked Wind Farms M. Vali et al. 10.1109/TCST.2021.3053776
27. Utilizing WFSim to Investigate the Impact of Optimal Wind Farm Layout and Inter-Field Wake on Average Power G. Li et al. 10.3390/jmse12081353
28. Heteroscedastic Bayesian Optimisation for Active Power Control of Wind Farms* K. Hoang et al. 10.1016/j.ifacol.2023.10.1164
29. Short-term wind forecasting via surface pressure measurements: Stochastic modeling and sensor placement S. Abootorabi et al. 10.1063/5.0216465
30. Transfer learning in building dynamics prediction G. Chaudhary et al. 10.1016/j.enbuild.2025.115384
31. Digital twin of wind farms via physics-informed deep learning J. Zhang & X. Zhao 10.1016/j.enconman.2023.117507
32. Evaluation of LES-based time-decoupled model-predictive control in different wind farm layouts N. Janssens & J. Meyers 10.1088/1742-6596/2767/3/032011
33. Dynamic wind farm flow control using free-vortex wake models M. van den Broek et al. 10.5194/wes-9-721-2024
34. Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets S. De et al. 10.1007/s00466-023-02272-4
35. A low-fidelity dynamic wind farm model for simulating time-varying wind conditions and floating platform motion A. Kheirabadi & R. Nagamune 10.1016/j.oceaneng.2021.109313
36. Adjoint-based model predictive control for optimal energy extraction in waked wind farms M. Vali et al. 10.1016/j.conengprac.2018.11.005
37. Joint state-parameter estimation for a control-oriented LES wind farm model B. Doekemeijer et al. 10.1088/1742-6596/1037/3/032013
38. Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control B. Doekemeijer et al. 10.5194/wes-3-749-2018
39. Deep learning-aided model predictive control of wind farms for AGC considering the dynamic wake effect K. Chen et al. 10.1016/j.conengprac.2021.104925
40. Pseudo-2D RANS: A LiDAR-driven mid-fidelity model for simulations of wind farm flows S. Letizia & G. Iungo 10.1063/5.0076739
41. Wind farm wake modeling based on deep convolutional conditional generative adversarial network J. Zhang & X. Zhao 10.1016/j.energy.2021.121747
42. 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
43. Data-Driven Wind Farm Control via Multiplayer Deep Reinforcement Learning H. Dong & X. Zhao 10.1109/TCST.2022.3223185
44. 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
45. A novel dynamic wind farm wake model based on deep learning J. Zhang & X. Zhao 10.1016/j.apenergy.2020.115552
46. Fast Control-Oriented Dynamic Linear Model of Wind Farm Flow and Operation J. Kazda & N. Cutululis 10.3390/en11123346
47. Adjoint optimisation for wind farm flow control with a free-vortex wake model M. van den Broek et al. 10.1016/j.renene.2022.10.120
48. Maximizing wind farm production through pitch control using graph neural networks and hybrid learning methods Y. Huo et al. 10.1049/rpg2.13133
49. Large-eddy simulation study of wind farm active power control with a coordinated load distribution M. Vali et al. 10.1088/1742-6596/1037/3/032018
50. Deep Neural Learning Based Distributed Predictive Control for Offshore Wind Farm Using High-Fidelity LES Data X. Yin & X. Zhao 10.1109/TIE.2020.2979560
51. Dynamic wind farm wake modeling based on a Bilateral Convolutional Neural Network and high-fidelity LES data R. Li et al. 10.1016/j.energy.2022.124845
52. Model Predictive Control for Wind Farm Power Tracking With Deep Learning-Based Reduced Order Modeling K. Chen et al. 10.1109/TII.2022.3157302
53. An active power control approach for wake-induced load alleviation in a fully developed wind farm boundary layer M. Vali et al. 10.5194/wes-4-139-2019
54. Reinforcement Learning-Based Multiobjective Control of Grid-Connected Wind Farms Y. Huang & X. Zhao 10.1109/TII.2024.3359420
55. Wind Farm Control via Offline Reinforcement Learning With Adversarial Training Y. Huang & X. Zhao 10.1109/TASE.2025.3548565
56. Wind Farm Power Generation Control Via Double-Network-Based Deep Reinforcement Learning J. Xie et al. 10.1109/TII.2021.3095563
57. Wind farm control technologies: from classical control to reinforcement learning H. Dong et al. 10.1088/2516-1083/ac6cc1
58. Decentralized yaw optimization for maximizing wind farm production based on deep reinforcement learning Z. Deng et al. 10.1016/j.enconman.2023.117031
59. Yaw Optimisation for Wind Farm Production Maximisation Based on a Dynamic Wake Model Z. Deng et al. 10.3390/en16093932
60. pymodconn: A python package for developing modular sequence-to-sequence control-oriented deep neural networks G. Chaudhary et al. 10.1016/j.softx.2023.101599
61. A physics-guided machine learning framework for real-time dynamic wake prediction of wind turbines B. Li et al. 10.1063/5.0194764
62. Quantification of 3D spatiotemporal inhomogeneity for wake characteristics with validations from field measurement and wind tunnel test X. Gao et al. 10.1016/j.energy.2022.124277
63. Development and validation of a health-aware floating offshore wind farm simulation platform: FOWLTY Y. Peña-Sanchez et al. 10.1016/j.weer.2024.100008
64. A Wake Modeling Paradigm for Wind Farm Design and Control C. Shapiro et al. 10.3390/en12152956
65. State Estimation for a Tethered Underwater Kite D. Bhattacharjee & H. Fathy 10.1016/j.ifacol.2025.01.029
66. Intelligent wind farm control via deep reinforcement learning and high-fidelity simulations H. Dong et al. 10.1016/j.apenergy.2021.116928
67. A Simulation Model for Providing Analysis of Wind Farms Frequency and Voltage Regulation Services in an Electrical Power System H. Bialas et al. 10.3390/en14082250
68. Extending the dynamic wake meandering model in HAWC2Farm: a comparison with field measurements at the Lillgrund wind farm J. Liew et al. 10.5194/wes-8-1387-2023
69. Dynamic wake steering control for maximizing wind farm power based on a physics-guided neural network dynamic wake model B. Li et al. 10.1063/5.0223631
70. Study on equivalent fatigue damage of two in-a-line wind turbines under yaw-based optimum control H. Meng et al. 10.1080/15435075.2021.2023887
71. 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
72. Quantification of parameter uncertainty in wind farm wake modeling J. Zhang & X. Zhao 10.1016/j.energy.2020.117065
73. Towards real-time optimal control of wind farms using large-eddy simulations N. Janssens & J. Meyers 10.5194/wes-9-65-2024
74. Optimal closed-loop wake steering – Part 1: Conventionally neutral atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-5-1315-2020
75. Towards machine learning applications for structural load and power assessment of wind turbine: An engineering perspective Q. Wang et al. 10.1016/j.enconman.2024.119275
76. Turbulence and Control of Wind Farms C. Shapiro et al. 10.1146/annurev-control-070221-114032
77. Wind-Farm Power Tracking Via Preview-Based Robust Reinforcement Learning H. Dong & X. Zhao 10.1109/TII.2021.3093300
78. A control-oriented dynamic wind farm model: WFSim S. Boersma et al. 10.5194/wes-3-75-2018