Sensitivity analysis and Bayesian calibration of a dynamic wind farm control model: FLORIDyn

Vinit V. Dighe*, Marcus Becker, Tuhfe Göcmen, Benjamin Sanderse, Jan Willem Van Wingerden

*Corresponding author for this work

Research output: Contribution to journalConference articleScientificpeer-review

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FLORIDyn is a parametric control-oriented dynamic model suitable to predict the dynamic wake interactions between wind turbines in a wind farm. In order to improve the accuracy of FLORIDyn, this study proposes to calibrate the tuning parameters present in the model by employing a probabilistic setting using the UQ4WIND framework. The strategy relies on constructing a surrogate model (based on polynomial chaos expansion), which is then used to perform both global sensitivity analysis and Bayesian calibration. For our analysis, a nine wind turbine configuration in a yawed setting constitutes the test case. The results of sensitivity analysis offer valuable insight into the time-dependent influence of the model parameters onto the model output. The model parameter tied to the turbine efficiency appear to be the most sensitive parameter affecting the model output. The calibrated FLORIDyn model using the Bayesian approach yield predictions much closer to the measurement data, which is equipped with an uncertainty estimate.

Original languageEnglish
Article number022062
Number of pages10
JournalJournal of Physics: Conference Series
Issue number2
Publication statusPublished - 2022
Event2022 Science of Making Torque from Wind, TORQUE 2022 - Delft, Netherlands
Duration: 1 Jun 20223 Jun 2022


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