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.
Original language | English |
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Title of host publication | Journal of Physics: Conference Series |
Subtitle of host publication | The Science of Making Torque from Wind (TORQUE 2018) |
Place of Publication | Bristol, UK |
Publisher | IOP Publishing |
Number of pages | 8 |
Volume | 1037 |
DOIs | |
Publication status | Published - 2018 |
Event | TORQUE 2018: The Science of Making Torque from Wind - Milano, Italy Duration: 20 Jun 2018 → 22 Jun 2018 http://www.torque2018.org/ |
Publication series
Name | Journal of Physics: Conference Series |
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Publisher | IOP Publishing Ltd. |
ISSN (Print) | 1742-6588 |
Conference
Conference | TORQUE 2018 |
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Abbreviated title | TORQUE 2018 |
Country/Territory | Italy |
City | Milano |
Period | 20/06/18 → 22/06/18 |
Internet address |