Wind turbines in a wind farm operate individually to maximize their own power regardless of the impact of aerodynamic interactions on neighboring turbines. There is the potential to increase power and reduce overall structural loads by properly coordinating turbines. To perform control design and analysis, a model needs to be of low computational cost, but retains the necessary dynamics seen in high-fidelity models. The objective of this work is to obtain a reduced-order model that represents the full-order flow computed using a high-fidelity model. A variety of methods, including proper orthogonal decomposition and dynamic mode decomposition, can be used to extract the dominant flow structures and obtain a reduced-order model. In this paper, we combine proper orthogonal decomposition with a system identification technique to produce an input-output reduced-order model. This technique is used to construct a reduced-order model of the flow within a two-turbine array computed using a large-eddy simulation.
|Original language||English (US)|
|Title of host publication||2016 American Control Conference, ACC 2016|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||7|
|State||Published - Jul 28 2016|
|Event||2016 American Control Conference, ACC 2016 - Boston, United States|
Duration: Jul 6 2016 → Jul 8 2016
|Name||Proceedings of the American Control Conference|
|Other||2016 American Control Conference, ACC 2016|
|Period||7/6/16 → 7/8/16|
Bibliographical noteFunding Information:
ACKNOWLEDGMENTS: This work was supported by the National Science Foundation under Grant No. NSF-CMMI-1254129 entitled CAREER: Probabilistic Tools for High Reliability Monitoring and Control of Wind Farms.
© 2016 American Automatic Control Council (AACC).
Copyright 2016 Elsevier B.V., All rights reserved.