87 citations as recorded by crossref.

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6. A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes M. Amiri et al.
7. Dual-Stage MPC-Based AGC for Wind Farm Considering Aerodynamic Interactions Z. Huang & W. Wu
8. Maximization of the Power Production of an Offshore Wind Farm R. Balakrishnan & S. Hur
9. Comparative Analysis of Wind Farm Simulators for Wind Farm Control M. Kim et al.
10. A medium-fidelity wake model of floating wind turbines considering the double Gaussian virtual force S. Wei et al.
11. LES Study of Wake Meandering in Different Atmospheric Stabilities and Its Effects on Wind Turbine Aerodynamics X. Ning & D. Wan
12. MPC-Based Fatigue Load Suppression of Waked Wind Farm With 2Dof WT Control Strategy W. Chen et al.
13. Integer programming for optimal yaw control of wind farms F. Bestehorn et al.
14. Fault-tolerant effective wind speed estimation in wind farms via EKF and deep learning fusion S. Mousavi et al.
15. Influence of atmospheric stability on wind farm performance in complex terrain W. Radünz et al.
16. Transfer learning in building dynamics prediction G. Chaudhary et al.
17. Digital twin of wind farms via physics-informed deep learning J. Zhang & X. Zhao
18. Evaluation of LES-based time-decoupled model-predictive control in different wind farm layouts N. Janssens & J. Meyers
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20. A low-fidelity dynamic wind farm model for simulating time-varying wind conditions and floating platform motion A. Kheirabadi & R. Nagamune
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22. Wind farm fluid mechanics for high-penetration wind energy X. Yang et al.
23. Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control B. Doekemeijer et al.
24. Deep learning-aided model predictive control of wind farms for AGC considering the dynamic wake effect K. Chen et al.
25. Wind farm wake modeling based on deep convolutional conditional generative adversarial network J. Zhang & X. Zhao
26. FlowFormer: Toward a foundation model for full-flow-field wind farm wake modeling R. Li et al.
27. A quantitative review of wind farm control with the objective of wind farm power maximization A. Kheirabadi & R. Nagamune
28. Fast Control-Oriented Dynamic Linear Model of Wind Farm Flow and Operation J. Kazda & N. Cutululis
29. Adjoint optimisation for wind farm flow control with a free-vortex wake model M. van den Broek et al.
30. Dynamic wind farm wake modeling based on a Bilateral Convolutional Neural Network and high-fidelity LES data R. Li et al.
31. Model Predictive Control for Wind Farm Power Tracking With Deep Learning-Based Reduced Order Modeling K. Chen et al.
32. Wind Farm Control via Offline Reinforcement Learning With Adversarial Training Y. Huang & X. Zhao
33. Distributed collaborative model predictive control based on computationally feasible mesh wind farms J. Liu et al.
34. pymodconn: A python package for developing modular sequence-to-sequence control-oriented deep neural networks G. Chaudhary et al.
35. Quantification of 3D spatiotemporal inhomogeneity for wake characteristics with validations from field measurement and wind tunnel test X. Gao et al.
36. Development and validation of a health-aware floating offshore wind farm simulation platform: FOWLTY Y. Peña-Sanchez et al.
37. A Wake Modeling Paradigm for Wind Farm Design and Control C. Shapiro et al.
38. Dynamic wake steering control for maximizing wind farm power based on a physics-guided neural network dynamic wake model B. Li et al.
39. Study on equivalent fatigue damage of two in-a-line wind turbines under yaw-based optimum control H. Meng et al.
40. Towards fine tuning wake steering policies in the field: an imitation-based approach C. Bizon Monroc et al.
41. Quantification of parameter uncertainty in wind farm wake modeling J. Zhang & X. Zhao
42. Towards real-time optimal control of wind farms using large-eddy simulations N. Janssens & J. Meyers
43. Optimal closed-loop wake steering – Part 1: Conventionally neutral atmospheric boundary layer conditions M. Howland et al.
44. Towards machine learning applications for structural load and power assessment of wind turbine: An engineering perspective Q. Wang et al.
45. Turbulence and Control of Wind Farms C. Shapiro et al.
46. Wind-Farm Power Tracking Via Preview-Based Robust Reinforcement Learning H. Dong & X. Zhao
47. Stochastic Dynamical Modeling of Wind Farm Turbulence A. Bhatt et al.
48. Free-vortex models for wind turbine wakes under yaw misalignment – a validation study on far-wake effects M. van den Broek et al.
49. A new analytical wind turbine wake model considering the effects of coriolis force and yawed conditions R. Snaiki & S. Makki
50. Hierarchical clustering-based deep reinforcement learning for adaptive frequency control of wind farm S. Tian et al.
51. Wind farm flow control: prospects and challenges J. Meyers et al.
52. Composite Experience Replay-Based Deep Reinforcement Learning With Application in Wind Farm Control H. Dong & X. Zhao
53. Optimal combined wake and active power control of large‐scale wind farm considering available power W. Chen et al.
54. Model predictive control strategy in waked wind farms for optimal fatigue loads C. Zhong et al.
55. Model-Optimized Dispatch for Closed-Loop Power Control of Waked Wind Farms J. Kazda & N. Cutululis
56. Wind farm supervisory controller design for power optimization in localized areas using adaptive learning game theory (ALGT) V. Fazlollahi et al.
57. Reinforcement Learning-Based Wind Farm Control: Toward Large Farm Applications via Automatic Grouping and Transfer Learning H. Dong & X. Zhao
58. A constrained wind farm controller providing secondary frequency regulation: An LES study S. Boersma et al.
59. Dynamic Flow Modelling for Model-Predictive Wind Farm Control M. van den Broek & J. Wingerden
60. The area localized coupled model for analytical mean flow prediction in arbitrary wind farm geometries G. Starke et al.
61. An Efficient Hybrid Evolutionary Algorithm for Enhanced Wind Energy Capture M. Rashid et al.
62. Model Predictive Active Power Control for Optimal Structural Load Equalization in Waked Wind Farms M. Vali et al.
63. Utilizing WFSim to Investigate the Impact of Optimal Wind Farm Layout and Inter-Field Wake on Average Power G. Li et al.
64. Heteroscedastic Bayesian Optimisation for Active Power Control of Wind Farms* K. Hoang et al.
65. Short-term wind forecasting via surface pressure measurements: Stochastic modeling and sensor placement S. Abootorabi et al.
66. Dynamic wind farm flow control using free-vortex wake models M. van den Broek et al.
67. Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets S. De et al.
68. Joint state-parameter estimation for a control-oriented LES wind farm model B. Doekemeijer et al.
69. Pseudo-2D RANS: A LiDAR-driven mid-fidelity model for simulations of wind farm flows S. Letizia & G. Iungo
70. Koopman Model Predictive Control for Wind Farm Yield Optimization with Combined Thrust and Yaw Control A. Dittmer et al.
71. Data-Driven Wind Farm Control via Multiplayer Deep Reinforcement Learning H. Dong & X. Zhao
72. A novel dynamic wind farm wake model based on deep learning J. Zhang & X. Zhao
73. Maximizing wind farm production through pitch control using graph neural networks and hybrid learning methods Y. Huo et al.
74. Large-eddy simulation study of wind farm active power control with a coordinated load distribution M. Vali et al.
75. Deep Neural Learning Based Distributed Predictive Control for Offshore Wind Farm Using High-Fidelity LES Data X. Yin & X. Zhao
76. An active power control approach for wake-induced load alleviation in a fully developed wind farm boundary layer M. Vali et al.
77. Reinforcement Learning-Based Multiobjective Control of Grid-Connected Wind Farms Y. Huang & X. Zhao
78. Wind Farm Power Generation Control Via Double-Network-Based Deep Reinforcement Learning J. Xie et al.
79. Wind farm control technologies: from classical control to reinforcement learning H. Dong et al.
80. Decentralized yaw optimization for maximizing wind farm production based on deep reinforcement learning Z. Deng et al.
81. Yaw Optimisation for Wind Farm Production Maximisation Based on a Dynamic Wake Model Z. Deng et al.
82. A physics-guided machine learning framework for real-time dynamic wake prediction of wind turbines B. Li et al.
83. State Estimation for a Tethered Underwater Kite D. Bhattacharjee & H. Fathy
84. Intelligent wind farm control via deep reinforcement learning and high-fidelity simulations H. Dong et al.
85. A Simulation Model for Providing Analysis of Wind Farms Frequency and Voltage Regulation Services in an Electrical Power System H. Bialas et al.
86. Extending the dynamic wake meandering model in HAWC2Farm: a comparison with field measurements at the Lillgrund wind farm J. Liew et al.
87. LPV Model Predictive Control for Offshore Wind Farms Considering Wake Delay Characteristics Y. Liu et al.