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Article

Wind Farm Yaw Optimization via Random Search Algorithm

by 1,*, 1, 1,2 and 1
1
Department of Mechanical Engineering, California State University, Los Angeles, CA 90032, USA
2
School of Aeronautics, Northwestern Polytechnical University, Xian 710072, China
*
Author to whom correspondence should be addressed.
Energies 2020, 13(4), 865; https://doi.org/10.3390/en13040865
Received: 9 December 2019 / Revised: 6 February 2020 / Accepted: 11 February 2020 / Published: 16 February 2020
(This article belongs to the Special Issue State of the Art of Wind Farm Optimization)
One direction in optimizing wind farm production is reducing wake interactions from upstream turbines. This can be done by optimizing turbine layout as well as optimizing turbine yaw and pitch angles. In particular, wake steering by optimizing yaw angles of wind turbines in farms has received significant attention in recent years. One of the challenges in yaw optimization is developing fast optimization algorithms which can find good solutions in real-time. In this work, we developed a random search algorithm to optimize yaw angles. Optimization was performed on a layout of 39 turbines in a 2 km by 2 km domain. Algorithm specific parameters were tuned for highest solution quality and lowest computational cost. Testing showed that this algorithm can find near-optimal (<1% of best known solutions) solutions consistently over multiple runs, and that quality solutions can be found under 200 iterations. Empirical results show that as wind farm density increases, the potential for yaw optimization increases significantly, and that quality solutions are likely to be plentiful and not unique. View Full-Text
Keywords: yaw optimization; wake steering; wind farm yaw optimization; wake steering; wind farm
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MDPI and ACS Style

Kuo, J.; Pan, K.; Li, N.; Shen, H. Wind Farm Yaw Optimization via Random Search Algorithm. Energies 2020, 13, 865. https://doi.org/10.3390/en13040865

AMA Style

Kuo J, Pan K, Li N, Shen H. Wind Farm Yaw Optimization via Random Search Algorithm. Energies. 2020; 13(4):865. https://doi.org/10.3390/en13040865

Chicago/Turabian Style

Kuo, Jim; Pan, Kevin; Li, Ni; Shen, He. 2020. "Wind Farm Yaw Optimization via Random Search Algorithm" Energies 13, no. 4: 865. https://doi.org/10.3390/en13040865

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