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Ensemble-Based Flow Field Estimation Using the Dynamic Wind Farm Model FLORIDyn

Author

Listed:
  • Marcus Becker

    (Delft Center for Systems and Control, TU Delft, Mekelweg 2, 2628 CD Delft, The Netherlands)

  • Dries Allaerts

    (Faculty of Aerospace Engineering, TU Delft, Kluyverweg 1, 2629 HS Delft, The Netherlands)

  • Jan-Willem van Wingerden

    (Delft Center for Systems and Control, TU Delft, Mekelweg 2, 2628 CD Delft, The Netherlands)

Abstract

Wind farm control methods allow for a more flexible use of wind power plants over the baseline operation. They can be used to increase the power generated, to track a reference power signal or to reduce structural loads on a farm-wide level. Model-based control strategies have the advantage that prior knowledge can be included, for instance by simulating the current flow field state into the near future to take adequate control actions. This state needs to describe the real system as accurately as possible. This paper discusses what state estimation methods are suitable for wind farm flow field estimation and how they can be applied to the dynamic engineering model FLORIDyn. In particular, we derive an Ensemble Kalman Filter framework which can identify heterogeneous and changing wind speeds and wind directions across a wind farm. It does so based on the power generated by the turbines and wind direction measurements at the turbine locations. Next to the states, this framework quantifies uncertainty for the resulting state estimates. We also highlight challenges that arise when ensemble methods are applied to particle-based flow field simulations. The development of a flow field estimation framework for dynamic low-fidelity wind farm models is an essential step toward real-time dynamic model-based closed-loop wind farm control.

Suggested Citation

  • Marcus Becker & Dries Allaerts & Jan-Willem van Wingerden, 2022. "Ensemble-Based Flow Field Estimation Using the Dynamic Wind Farm Model FLORIDyn," Energies, MDPI, vol. 15(22), pages 1-23, November.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8589-:d:974855
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    References listed on IDEAS

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    1. Carl R. Shapiro & Genevieve M. Starke & Charles Meneveau & Dennice F. Gayme, 2019. "A Wake Modeling Paradigm for Wind Farm Design and Control," Energies, MDPI, vol. 12(15), pages 1-19, August.
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