Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

On the potential of reduced order models for wind farm control: a Koopman dynamic mode decomposition approach

Version 1 : Received: 14 October 2020 / Approved: 16 October 2020 / Online: 16 October 2020 (14:29:54 CEST)

How to cite: Cassamo, N.; van Wingerden, J. On the potential of reduced order models for wind farm control: a Koopman dynamic mode decomposition approach. Preprints 2020, 2020100352 (doi: 10.20944/preprints202010.0352.v1). Cassamo, N.; van Wingerden, J. On the potential of reduced order models for wind farm control: a Koopman dynamic mode decomposition approach. Preprints 2020, 2020100352 (doi: 10.20944/preprints202010.0352.v1).

Abstract

The high dimensions and governing non linear dynamics in wind farm systems make the design of numerical optimal controllers computationally expensive. A possible pathway to circumvent this challenge lies in finding reduced order models which can accurately embed the existing non linearities. The work here presented applies the ideas motivated by non linear dynamical systems theory - the Koopman Operator - to an innovative algorithm in the context of wind farm systems - Input Output Dynamic Mode Decomposition - to improve on the ability to model the aerodynamic interaction between wind turbines in a wind farm and uncover insights into the existing dynamics. It is shown that a reduced order linear state space model can reproduce the downstream turbine generator power dynamics and reconstruct the upstream turbine wake. It is further shown that the fit can be improved by judiciously choosing the Koopman observables used in the IODMD algorithm without jeopardizing the models ability to rebuild the turbine wake. The extensions to the IODMD algorithm provide an important step towards the design of linear reduced order models which can accurately reproduce the dynamics in a wind farm.

Subject Areas

Wind Farm Control; Axial Induction Control; Dynamic Mode Decomposition; Koopman Operator Theory; Reduced Order Model

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