Abstract
Physical interactions among wind turbines, called wake effects, are known to be one of the significant factors that affect power generation performance in wind power systems. Among several wake modeling approaches, physics-based engineering models, such as Jensen’s model, have been widely used due to their computational tractability. Although substantial efforts have been made to improve the accuracy of engineering wake models, few studies suggest calibrating the model parameters in the literature. We propose a new data-driven calibration approach for adjusting the model parameters using real operational data.
Original language | English (US) |
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Title of host publication | Wind Energy Symposium |
Publisher | American Institute of Aeronautics and Astronautics Inc, AIAA |
ISBN (Print) | 9781624105227 |
DOIs | |
State | Published - 2018 |
Event | Wind Energy Symposium, 2018 - Kissimmee, United States Duration: Jan 8 2018 → Jan 12 2018 |
Publication series
Name | Wind Energy Symposium, 2018 |
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Other
Other | Wind Energy Symposium, 2018 |
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Country/Territory | United States |
City | Kissimmee |
Period | 1/8/18 → 1/12/18 |
Funding
This work was supported by the National Science Foundation (Grant No. CMMI-1362513, CMMI-1536924, and IIS-1741166) and the University of Michigan MCubed Grant.
ASJC Scopus subject areas
- Renewable Energy, Sustainability and the Environment
- Mechanical Engineering