Wind Farm Yaw Optimization via Random Search Algorithm

Jim Kuo, Kevin Pan, Ni Li and He Shen
Additional contact information
Jim Kuo: Department of Mechanical Engineering, California State University, Los Angeles, CA 90032, USA
Kevin Pan: Department of Mechanical Engineering, California State University, Los Angeles, CA 90032, USA
Ni Li: Department of Mechanical Engineering, California State University, Los Angeles, CA 90032, USA
He Shen: Department of Mechanical Engineering, California State University, Los Angeles, CA 90032, USA

Energies, 2020, vol. 13, issue 4, 1-15

Abstract: 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.

Keywords: yaw optimization; wake steering; wind farm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/4/865/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/4/865/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:4:p:865-:d:321358

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().