TY - GEN
T1 - Data-driven parameter calibration in wake models
AU - Liu, Bingjie
AU - Byon, Eunshin
AU - Plumlee, Matthew
N1 - Funding Information:
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.
Publisher Copyright:
© 2018 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
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U2 - 10.2514/6.2018-2017
DO - 10.2514/6.2018-2017
M3 - Conference contribution
AN - SCOPUS:85141641603
SN - 9781624105227
T3 - Wind Energy Symposium, 2018
BT - Wind Energy Symposium
PB - American Institute of Aeronautics and Astronautics Inc, AIAA
T2 - Wind Energy Symposium, 2018
Y2 - 8 January 2018 through 12 January 2018
ER -