Aerodynamic Load Estimation in Wind Turbine Drivetrains Using a Bayesian Data Assimilation Approach
摘要
This work explores a Bayesian data assimilation approach to estimate the aerodynamic input force on a wind turbine drivetrain. To this end, a high-fidelity wind turbine drivetrain model is created in SIMPACK to generate synthetic data. The NREL 5 MW wind turbine is considered as a case study in this work. The synthetic data include generator rotational speed, generator torque, rotor rotational speed, which are simulated and used as measurement data. A low-fidelity model of the drivetrain is developed for load estimation to accelerate the estimation process. The Bayesian data assimilation approach is employed to integrate synthetic data with the low-fidelity model to estimate aerodynamic input load.