Data-driven Reduced Order Model for prediction of wind turbine wakes

Wake Conference 2015

Iungo, G.V.^1 ; Santoni-Ortiz, C.^1 ; Abkar, M.^2 ; Porté-Agel, F.^2 ; Rotea, M.A.^1 ; Leonardi, S.^1

University of Texas at Dallas, Mechanical Engineering Department, Richardson

TX

75080, United States^1

Ecole Polytechnique Fédérale de Lausanne (EPFL), Wind Engineering and Renewable Energy (WIRE) Lab, Lausanne, Switzerland^2

关键词: Computational costs;    Control and optimization;    Data-driven algorithm;    Dynamic mode decompositions;    Real-time application;    Reduced order models;    Time-marching algorithms;    Wind turbine wakes;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/625/1/012009/pdf
DOI  :  10.1088/1742-6596/625/1/012009

来源: IOP

【 摘 要 】

In this paper a new paradigm for prediction of wind turbine wakes is proposed, which is based on a reduced order model (ROM) embedded in a Kalman filter. The ROM is evaluated by means of dynamic mode decomposition performed on high fidelity LES numerical simulations of wind turbines operating under different operational regimes. The ROM enables to capture the main physical processes underpinning the downstream evolution and dynamics of wind turbine wakes. The ROM is then embedded within a Kalman filter in order to produce a time-marching algorithm for prediction of wind turbine wake flows. This data-driven algorithm enables data assimilation of new measurements simultaneously to the wake prediction, which leads to an improved accuracy and a dynamic update of the ROM in presence of emerging coherent wake dynamics observed from new available data. Thanks to its low computational cost, this numerical tool is particularly suitable for real-time applications, control and optimization of large wind farms.