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

Claudia Muscari*, Paolo Schito, Axelle Viré, Alberto Zasso, Jan Willem van Wingerden*

*Corresponding author for this work

Research output: Contribution to journalConference articleScientificpeer-review

15 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)8414-8419
Number of pages6
Issue number2
Publication statusPublished - 2023
Event22nd IFAC World Congress - Yokohama, Japan
Duration: 9 Jul 202314 Jul 2023


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


Dive into the research topics of 'Physics-informed data-driven reduced-order models for Dynamic Induction Control'. Together they form a unique fingerprint.

Cite this