29 citations as recorded by crossref.

1. Influence of incoming turbulent scales on the wind turbine wake: A large-eddy simulation study D. Vahidi & F. Porté-Agel 10.1063/5.0222372
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4. A new wake‐merging method for wind‐farm power prediction in the presence of heterogeneous background velocity fields L. Lanzilao & J. Meyers 10.1002/we.2669
5. LiDAR and SCADA data processing for interacting wind turbine wakes with comparison to analytical wake models A. Hegazy et al. 10.1016/j.renene.2021.09.019
6. Stochastic gradient descent for wind farm optimization J. Quick et al. 10.5194/wes-8-1235-2023
7. A Meandering-Capturing Wake Model Coupled to Rotor-Based Flow-Sensing for Operational Wind Farm Flow Prediction M. Lejeune et al. 10.3389/fenrg.2022.884068
8. Launch of the FarmConners Wind Farm Control benchmark for code comparison T. Göçmen et al. 10.1088/1742-6596/1618/2/022040
9. Wind farm control ‐ Part I: A review on control system concepts and structures L. Andersson et al. 10.1049/rpg2.12160
10. Analytical solutions for yawed wind-turbine wakes with application to wind-farm power optimization by active yaw control Z. Zhang et al. 10.1016/j.oceaneng.2024.117691
11. A New Study on the Effect of the Partial Wake Generated in a Wind Farm S. Zergane et al. 10.3390/en17061498
12. Study on the Effect of Yaw and Spacing in Onshore Wind Farms Considering Wake Effect C. Wang et al. 10.1016/j.kscej.2024.100141
13. Experimental analysis of time delays in wind turbine wake interactions S. Macrì et al. 10.1088/1742-6596/1618/6/062058
14. FarmConners wind farm flow control benchmark – Part 1: Blind test results T. Göçmen et al. 10.5194/wes-7-1791-2022
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16. 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
17. New genetic gray wolf optimizer with a random selective mutation for wind farm layout optimization M. Amaro Pinazo 10.1016/j.heliyon.2024.e40135
18. Stochastic Dynamical Modeling of Wind Farm Turbulence A. Bhatt et al. 10.3390/en16196908
19. Optimal tuning of engineering wake models through lidar measurements L. Zhan et al. 10.5194/wes-5-1601-2020
20. Wake impact of constructing a new offshore wind farm zone on an existing downwind cluster: a case study of the Belgian Princess Elisabeth zone using FLORIS W. Munters et al. 10.1088/1742-6596/2265/2/022049
21. Evaluation of the potential for wake steering for U.S. land-based wind power plants D. Bensason et al. 10.1063/5.0039325
22. Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance E. Simley et al. 10.5194/wes-6-1427-2021
23. Wake effect parameter calibration with large-scale field operational data using stochastic optimization P. Jain et al. 10.1016/j.apenergy.2023.121426
24. Wind farm flow control: prospects and challenges J. Meyers et al. 10.5194/wes-7-2271-2022
25. Single and multi-row turbine performance in bounded shear flow M. Juniper & T. Nishino 10.1017/jfm.2023.49
26. Sensitivity of Wake Modelling Setups L. Kemme et al. 10.1088/1742-6596/2265/2/022007
27. Comparison of modular analytical wake models to the Lillgrund wind plant N. Hamilton et al. 10.1063/5.0018695
28. Verification of Prediction Method Based on Machine Learning under Wake Effect Using Real-Time Digital Simulator R. Park et al. 10.3390/en15249475
29. A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes M. Amiri et al. 10.1016/j.rser.2024.114279