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Title: Data-driven Parameter Calibration in Wake Models
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.  more » « less
Award ID(s):
1741166
PAR ID:
10384641
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceeding of the 36th Wind Energy Symposium, the 2018 American Institute of Aeronautics and Astronautics Science and Technology (AIAA SciTech) Forum and Exposition
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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