A meandering-capturing wake model coupled to rotor-based flow-sensing for operational wind farm flow estimation
(2023)
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- Authors
- Supervisors
- Chatelain, Philippe
- Abstract
- Driven by its low life-cycle greenhouse gases emissions and by falling costs, wind energy as progressively grown into the fastest developing renewable power technology. Yet, its current growth is still not rapid enough to sustain the current Net Zero by 2050 objectives therefore calling for innovative solutions that will support its accelerated deployment. Wind Farm Flow Control (WFFC) precisely supports this transition by aiming at improving the global performances of the wind power plant through the coordinated control of the turbines inside wind farms. In WFFC, the wind turbines leverage their knowledge about the flow in order to achieve given goals of power production or load alleviation. WFFC is consequently closely tied to the topic of flow awareness that is core to this thesis. More specifically, this thesis uses Large Eddy Simulations (LES) of wind farms to support the development, calibration and verification of an operational dynamic flow modeling framework. This framework, named OnWaRDS, brings together Lagrangian flow modeling and flow sensing. This allows for fast time estimates of the dynamic signature of the flow based only on the local information collected by the wind turbines. The proposed framework is first presented and then verified numerically under various operating and atmospheric conditions. This comparison reveals that it indeed provides additional insight into the wake physics when compared to the traditional steady state approaches: the characteristic signature of wake meandering along with the influence of large-scale fluctuations of the ambient flow are captured. We further demonstrate the good computational performances and modularity of the approach thereby potentially opening the way to future WFFC applications.
- Affiliations
Citations
Lejeune, M. (2023). A meandering-capturing wake model coupled to rotor-based flow-sensing for operational wind farm flow estimation. https://hdl.handle.net/2078.5/26101