A dynamic open-source model to investigate wake dynamics in response to wind farm flow control strategies

Journal Article (2025)
Author(s)

M. Becker (TU Delft - Team Jan-Willem van Wingerden)

Maxime Lejeune (Université Catholique de Louvain)

Philippe Chatelain (Université Catholique de Louvain)

D.J.N. Allaerts (TU Delft - Wind Energy)

Rafael Mudafort (National Renewable Energies Laboratory)

J.W. van Wingerden (TU Delft - Team Jan-Willem van Wingerden)

Research Group
Team Jan-Willem van Wingerden
DOI related publication
https://doi.org/10.5194/wes-2024-150
More Info
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Publication Year
2025
Language
English
Research Group
Team Jan-Willem van Wingerden
Issue number
6
Volume number
10
Pages (from-to)
1055-1075
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Abstract

Wind farm flow control (WFFC) is the discipline of manipulating the flow between wind turbines to achieve a farm-wide goal, like power maximization, power tracking or load mitigation. Specifically, steady-state control approaches have shown promising results in both theory and practice for power maximization. But how are they expected to perform in a dynamically changing environment? This paper presents an open-source wake modeling framework called OFF (abbreviated from the models OnWARDS, FLORIDyn and FLORIS). It allows the approximation of the performance of WFFC strategies in response to environmental changes at a low computational cost. It is rooted in previously published dynamic parametric engineering models and offers a flexible and adaptable platform to explore these models further. The presented study tests the modeling framework by investigating the performance of different wake steering controllers in a 10-turbine wind farm case study based on a subset of the Dutch wind farm Hollandse Kust Noord (HKN). The case study uses a 24 h wind direction time series based on field data and verifies subsets of the time series in a large-eddy simulation (LES). The results highlight how dependent yaw travel is on the controller settings and suggest where users can strike a balance between power gains and actuator usage. They also show the structural differences and similarities between steady-state and dynamic engineering models. The comparison to LES shows what timescales the surrogate models cover and how accurately. While steady-state models capture turbine power signal dynamics up to  Hz, the dynamic wake description can predict dynamics up to  Hz with a better correlation and normalized root-mean-square error. Further results show that the dynamic wake description is mainly advantageous over steady-state wake models for shorter periods (< 20 min). The paper also opens up discussion about the effectiveness of wind farm flow control in a time-marching manner as opposed to a steady-state viewpoint.