Articles | Volume 7, issue 5
Research article
30 Sep 2022
Research article |  | 30 Sep 2022

Multifidelity multiobjective optimization for wake-steering strategies

Julian Quick, Ryan N. King, Garrett Barter, and Peter E. Hamlington

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Cited articles

Allen, J., Young, E., Bortolotti, P., King, R., and Barter, G.: Blade planform design optimization to enhance turbine wake control, Wind Energy, 25, 811–830,, 2022. a
Andersson, L. E. and Imsland, L.: Real-time optimization of wind farms using modifier adaptation and machine learning, Wind Energ. Sci., 5, 885–896,, 2020. a, b, c
Annoni, J., Fleming, P., Scholbrock, A., Roadman, J., Dana, S., Adcock, C., Porte-Agel, F., Raach, S., Haizmann, F., and Schlipf, D.: Analysis of control-oriented wake modeling tools using lidar field results, Wind Energ. Sci., 3, 819–831,, 2018. a
Ariyarit, A. and Kanazaki, M.: Multi-Fidelity Multi-Objective Efficient Global Optimization Applied to Airfoil Design Problems, Applied Sciences, 17, 1318,, 2017. a, b
Bortolotti, P., Tarrés, H. C., Dykes, K. L., Merz, K., Sethuraman, L., Verelst, D., and Zahle, F.: IEA Wind TCP Task 37: Systems Engineering in Wind Energy – WP2.1 Reference Wind Turbines, United States,, 2019. a
Short summary
Wake steering is an emerging wind power plant control strategy where upstream turbines are intentionally yawed out of alignment with the incoming wind, thereby steering wakes away from downstream turbines. Trade-offs between the gains in power production and fatigue loads induced by this control strategy are the subject of continuing investigation. In this study, we present an optimization approach for efficiently exploring the trade-offs between power and loading during wake steering.
Final-revised paper