TY - GEN
T1 - Wind farm modeling and control using dynamic mode decomposition
AU - Annoni, Jennifer
AU - Nichols, Joseph W
AU - Seiler Jr, Peter J
PY - 2016/1/1
Y1 - 2016/1/1
N2 - The objective of this paper is to construct a low-order model of a wind farm that can be used for control design and analysis. There is a potential to use wind farm control to increase power and reduce overall structural loads by properly coordinating the turbines in a wind farm. To perform control design and analysis, a model of the wind farm needs to be constructed that has low computational complexity, but retains the necessary dynamics. High-fidelity computational fluid dynamic models provide accurate characterizations of complex flow dynamics in a wind farm, but are not suitable for control design due to their prohibitive computational complexity. A variety of methods, including proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD), can be used to extract the dominant flow structures and obtain reduced-order models. This paper introduces an extension to DMD that can handle problems with inputs and outputs. The proposed method, termed input-output dynamic mode decomposition (IODMD), uses a subspace identification technique to obtain models of low-complexity. Using this information, a low-order model of a wind farm is constructed that can be used for control design.
AB - The objective of this paper is to construct a low-order model of a wind farm that can be used for control design and analysis. There is a potential to use wind farm control to increase power and reduce overall structural loads by properly coordinating the turbines in a wind farm. To perform control design and analysis, a model of the wind farm needs to be constructed that has low computational complexity, but retains the necessary dynamics. High-fidelity computational fluid dynamic models provide accurate characterizations of complex flow dynamics in a wind farm, but are not suitable for control design due to their prohibitive computational complexity. A variety of methods, including proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD), can be used to extract the dominant flow structures and obtain reduced-order models. This paper introduces an extension to DMD that can handle problems with inputs and outputs. The proposed method, termed input-output dynamic mode decomposition (IODMD), uses a subspace identification technique to obtain models of low-complexity. Using this information, a low-order model of a wind farm is constructed that can be used for control design.
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M3 - Conference contribution
SN - 9781624103957
T3 - 34th Wind Energy Symposium
BT - 34th Wind Energy Symposium
PB - American Institute of Aeronautics and Astronautics Inc, AIAA
T2 - 34th Wind Energy Symposium, 2016
Y2 - 4 January 2016 through 8 January 2016
ER -