Physics-informed data-driven reduced-order models for Dynamic Induction Control

Physics-informed data-driven reduced-order models for Dynamic Induction Control

Muscari, C. (TU Delft Team Jan-Willem van Wingerden; Politecnico di Milano)
Schito, Paolo (Politecnico di Milano)
Viré, A.C. (TU Delft Wind Energy)
Zasso, Alberto (Politecnico di Milano)
van Wingerden, J.W. (TU Delft Team Jan-Willem van Wingerden)

2023

In this work, we find a reduced-order model for the wake of a wind turbine controlled with dynamic induction control. We use a physics-informed dynamic mode decomposition algorithm to reduce the model complexity in a way such that the physics of the wake mixing can be investigated and that the model itself can be easily embedded into control-oriented frameworks. After discussing the advantage of forcing the linear system resulting from the algorithm to be conservative (as a consequence of the periodicity of the pitch excitation) and the choice of observables, we describe a procedure for calculating the energy associated with individual modes. The considered data-set is composed of large eddy simulation (LES) results for a single DTU 10 MW wind turbine in uniform flow. Simulations were performed first with baseline control (for reference) and then with the Pulse and the Helix approaches with constant excitation amplitude and different excitation frequencies. The frequencies and energies associated with the resulting modes are discussed.

Computational Fluid Dynamics
Dynamic Mode Decomposition
reduced-order Models
Wake Mixing
Wind Farm Control

http://resolver.tudelft.nl/uuid:79c914de-fb3a-453b-825b-26894651f4b3

https://doi.org/10.1016/j.ifacol.2023.10.1036

IFAC-PapersOnLine, 56 (2), 8414-8419

22nd IFAC World Congress, 2023-07-09 → 2023-07-14, Yokohama, Japan

Institutional Repository

journal article

© 2023 C. Muscari, Paolo Schito, A.C. Viré, Alberto Zasso, J.W. van Wingerden