Wind farm modeling and control using dynamic mode decomposition

Jennifer Annoni, Joseph W Nichols, Peter J Seiler Jr

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations


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.

Original languageEnglish (US)
Title of host publication34th Wind Energy Symposium
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624103957
StatePublished - Jan 1 2016
Event34th Wind Energy Symposium, 2016 - San Diego, United States
Duration: Jan 4 2016Jan 8 2016

Publication series

Name34th Wind Energy Symposium


Other34th Wind Energy Symposium, 2016
Country/TerritoryUnited States
CitySan Diego


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