Joint state-parameter estimation for a control-oriented LES wind farm model

Conference paper (2018)

Authors

B.M. Doekemeijer Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering
S. Boersma Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering
L.Y. Pao University of Colorado
J.W. van Wingerden Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering
Research Group
Team Jan-Willem van Wingerden (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2018 B.M. Doekemeijer, S. Boersma, L. Y. Pao, J.W. van Wingerden
DOI: https://doi.org/10.1088/1742-6596/1037/3/032013
More Info
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Published Date

2018

Language

English

Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Jan-Willem van Wingerden

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.