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.
| Original language | English |
|---|---|
| Pages (from-to) | 1055-1075 |
| Number of pages | 21 |
| Journal | Wind Energy Science |
| Volume | 10 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 2025 |
Fingerprint
Dive into the research topics of 'A dynamic open-source model to investigate wake dynamics in response to wind farm flow control strategies'. Together they form a unique fingerprint.Datasets
-
Data belonging to the publication: A dynamic open-source model to investigate wake dynamics in response to wind farm flow control strategies
Becker, M. (Creator) & Lejeune, M. (Creator), TU Delft - 4TU.ResearchData, 7 Nov 2024
DOI: 10.4121/29c209fa-f2a4-456d-9353-67cf81be1aaa
Dataset/Software: Dataset
-
OFF framework, code underlying the publication A dynamic open-source model to investigate wake dynamics in response to wind farm flow control strategies
Becker, M. (Creator) & Lejeune, M. (Creator), TU Delft - 4TU.ResearchData, 7 Nov 2024
DOI: 10.4121/331f86fe-5acb-4a60-99cd-7f8f0135c200
Dataset/Software: Software
Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver