70 citations as recorded by crossref.

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30. Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets S. De et al. 10.1007/s00466-023-02272-4
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37. Wind farm wake modeling based on deep convolutional conditional generative adversarial network J. Zhang & X. Zhao 10.1016/j.energy.2021.121747
38. 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
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46. 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
47. 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
48. Model Predictive Control for Wind Farm Power Tracking With Deep Learning-Based Reduced Order Modeling K. Chen et al. 10.1109/TII.2022.3157302
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50. Reinforcement Learning-Based Multiobjective Control of Grid-Connected Wind Farms Y. Huang & X. Zhao 10.1109/TII.2024.3359420
51. Wind Farm Power Generation Control Via Double-Network-Based Deep Reinforcement Learning J. Xie et al. 10.1109/TII.2021.3095563
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56. A physics-guided machine learning framework for real-time dynamic wake prediction of wind turbines B. Li et al. 10.1063/5.0194764
57. 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
58. A Wake Modeling Paradigm for Wind Farm Design and Control C. Shapiro et al. 10.3390/en12152956
59. Intelligent wind farm control via deep reinforcement learning and high-fidelity simulations H. Dong et al. 10.1016/j.apenergy.2021.116928
60. 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
61. 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
62. 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
63. 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
64. 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
65. Quantification of parameter uncertainty in wind farm wake modeling J. Zhang & X. Zhao 10.1016/j.energy.2020.117065
66. Towards real-time optimal control of wind farms using large-eddy simulations N. Janssens & J. Meyers 10.5194/wes-9-65-2024
67. Optimal closed-loop wake steering – Part 1: Conventionally neutral atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-5-1315-2020
68. 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
69. Turbulence and Control of Wind Farms C. Shapiro et al. 10.1146/annurev-control-070221-114032
70. Wind-Farm Power Tracking Via Preview-Based Robust Reinforcement Learning H. Dong & X. Zhao 10.1109/TII.2021.3093300