Open AccessData assimilation for the prediction of wake trajectories within wind farms
Author(s) -
Maxime Lejeune,
Maud Moens,
Marion Coquelet,
Nicolas Coudou,
Philippe Chatelain
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1618/6/062055
Subject(s) - wake , turbine , limiting , wake turbulence , meteorology , computer science , data assimilation , computational fluid dynamics , large eddy simulation , focus (optics) , environmental science , marine engineering , aerospace engineering , engineering , physics , turbulence , mechanical engineering , optics
In this paper, we formulate a physics-based surrogate wake model in the framework of online wind farm control. A flow sensing module is coupled with a wake model in order to predict the behavior of the wake downstream of a wind turbine based on its loads, wind probe data and operating settings. Information about the incoming flow is recovered using flow sensing techniques and then fed to the wake model, which reconstructs the wake based on this limited set of information. Special focus is laid on limiting the number of input parameters while keeping the computational cost low in order to facilitate the tuning procedure. Once calibrated, comparison with high-fidelity numerical results retrieved from Large Eddy Simulation (LES) of a wind farm confirms the good potential of the approach for online wake prediction within farms. The two approaches are further compared in terms of their wake center and time-averaged speed deficit predictions demonstrating good agreement in the process.