Conference paper Open Access
Joint state-parameter estimation for a control-oriented LES wind farm model
Doekemeijer, Bart M;
Boersma, Sjoerd;
Pao, Lucy Y;
van Wingerden, Jan-Willem
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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- Publication date:
- August 3, 2018
- DOI:
-
- Keyword(s):
- wind farm control state estimation closed-loop kalman filtering
- Grants:
-
European Commission:
- CL-Windcon - Closed Loop Wind Farm Control (727477)
- Meeting:
- The Science of Making Torque from Wind (TORQUE 2018), Milan, Italy, 20-22 June 2018
- License (for files):
- Creative Commons Attribution Non Commercial Share Alike 3.0 Unported