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

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 ≈1/570 Hz, the dynamic wake description can predict dynamics up to ≈1/360 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.

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Publication Details

Journal
Wind energy science
Published
2025-06-11
DOI
https://doi.org/10.5194/wes-10-1055-2025
Citations
1
Primary Topic
Wind Energy Research and Development
Type
article
Field-Weighted Citation Impact
4.99

Funders

Controls
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article

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

Maxime Lejeune, Jan‐Willem van Wingerden, Rafael Mudafort, Dries Allaerts et al.
1 citations
Wind energy science
Wind Energy Research and Development
4.99
article

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

Maxime Lejeune, Jan‐Willem van Wingerden, Rafael Mudafort, Dries Allaerts, Marcus Becker, Philippe Chatelain
article en
1 citations

Abstract

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 ≈1/570 Hz, the dynamic wake description can predict dynamics up to ≈1/360 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.

Wind energy scienceVol. 10(6)
National Laboratory of the Rockies (US), UCLouvain (BE), Delft University of Technology (NL)
Nederlandse Organisatie voor Wetenschappelijk Onderzoek, HORIZON EUROPE European Research Council
Openalex Percentile: Top 6%
Wind Energy Research and Development
4.99
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