68 citations as recorded by crossref.

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17. LES Study of Wake Meandering in Different Atmospheric Stabilities and Its Effects on Wind Turbine Aerodynamics X. Ning & D. Wan 10.3390/su11246939
18. MPC-Based Fatigue Load Suppression of Waked Wind Farm With 2Dof WT Control Strategy W. Chen et al. 10.1109/TSTE.2024.3407775
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23. Model Predictive Active Power Control for Optimal Structural Load Equalization in Waked Wind Farms M. Vali et al. 10.1109/TCST.2021.3053776
24. 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
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26. Digital twin of wind farms via physics-informed deep learning J. Zhang & X. Zhao 10.1016/j.enconman.2023.117507
27. 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
28. Dynamic wind farm flow control using free-vortex wake models M. van den Broek et al. 10.5194/wes-9-721-2024
29. Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets S. De et al. 10.1007/s00466-023-02272-4
30. 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
31. Adjoint-based model predictive control for optimal energy extraction in waked wind farms M. Vali et al. 10.1016/j.conengprac.2018.11.005
32. Joint state-parameter estimation for a control-oriented LES wind farm model B. Doekemeijer et al. 10.1088/1742-6596/1037/3/032013
33. 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
34. 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
35. Pseudo-2D RANS: A LiDAR-driven mid-fidelity model for simulations of wind farm flows S. Letizia & G. Iungo 10.1063/5.0076739
36. Wind farm wake modeling based on deep convolutional conditional generative adversarial network J. Zhang & X. Zhao 10.1016/j.energy.2021.121747
37. 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
38. Data-Driven Wind Farm Control via Multiplayer Deep Reinforcement Learning H. Dong & X. Zhao 10.1109/TCST.2022.3223185
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40. A novel dynamic wind farm wake model based on deep learning J. Zhang & X. Zhao 10.1016/j.apenergy.2020.115552
41. Fast Control-Oriented Dynamic Linear Model of Wind Farm Flow and Operation J. Kazda & N. Cutululis 10.3390/en11123346
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43. Maximizing wind farm production through pitch control using graph neural networks and hybrid learning methods Y. Huo et al. 10.1049/rpg2.13133
44. 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
45. 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
46. 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
47. Model Predictive Control for Wind Farm Power Tracking With Deep Learning-Based Reduced Order Modeling K. Chen et al. 10.1109/TII.2022.3157302
48. 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
49. Reinforcement Learning-Based Multiobjective Control of Grid-Connected Wind Farms Y. Huang & X. Zhao 10.1109/TII.2024.3359420
50. Wind Farm Power Generation Control Via Double-Network-Based Deep Reinforcement Learning J. Xie et al. 10.1109/TII.2021.3095563
51. Wind farm control technologies: from classical control to reinforcement learning H. Dong et al. 10.1088/2516-1083/ac6cc1
52. Decentralized yaw optimization for maximizing wind farm production based on deep reinforcement learning Z. Deng et al. 10.1016/j.enconman.2023.117031
53. Yaw Optimisation for Wind Farm Production Maximisation Based on a Dynamic Wake Model Z. Deng et al. 10.3390/en16093932
54. 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
55. A physics-guided machine learning framework for real-time dynamic wake prediction of wind turbines B. Li et al. 10.1063/5.0194764
56. 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
57. A Wake Modeling Paradigm for Wind Farm Design and Control C. Shapiro et al. 10.3390/en12152956
58. Intelligent wind farm control via deep reinforcement learning and high-fidelity simulations H. Dong et al. 10.1016/j.apenergy.2021.116928
59. 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
60. 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
61. 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
62. 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
63. 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
64. Quantification of parameter uncertainty in wind farm wake modeling J. Zhang & X. Zhao 10.1016/j.energy.2020.117065
65. Towards real-time optimal control of wind farms using large-eddy simulations N. Janssens & J. Meyers 10.5194/wes-9-65-2024
66. Optimal closed-loop wake steering – Part 1: Conventionally neutral atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-5-1315-2020
67. Turbulence and Control of Wind Farms C. Shapiro et al. 10.1146/annurev-control-070221-114032
68. Wind-Farm Power Tracking Via Preview-Based Robust Reinforcement Learning H. Dong & X. Zhao 10.1109/TII.2021.3093300