Joint state-parameter estimation for a control-oriented LES wind farm model
B. M. Doekemeijer (TU Delft - Mechanical Engineering)
S. Boersma (TU Delft - Mechanical Engineering)
L. Y. Pao (University of Colorado - Boulder)
J. W. Van Wingerden (TU Delft - Mechanical Engineering)
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Abstract
Wind farm control research typically relies on computationally inexpensive, surrogate models for real-time optimization. However, due to the large time delays involved, changing atmospheric conditions and tough-to-model flow and turbine dynamics, these surrogate models need constant calibration. In this paper, a novel real-time (joint state-parameter) estimation solution for a medium-fidelity dynamical wind farm model is presented. In this work, we demonstrate the estimation of the freestream wind speed, local turbulence, and local wind field in a two-turbine wind farm using exclusively turbine power measurements. The estimator employs an Ensemble Kalman filter with a low computational cost of approximately 1.0 s per timestep on a dual-core notebook CPU. This work presents an essential building block for real-time wind farm control using computationally efficient dynamical wind farm models.
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