Model predictive control of wind turbine with aero-elastically tailored blades

Hussain, R and Yue, H and Recalde-Camacho, L (2022) Model predictive control of wind turbine with aero-elastically tailored blades. Journal of Physics: Conference Series, 2265 (3). 032084. ISSN 1742-6596 (https://doi.org/10.1088/1742-6596/2265/3/032084)

[thumbnail of Hussain-etal-JPCS-2022-Model-predictive-control-of-wind-turbine-with-aero-elastically-tailored-blades]
Preview
 Text. Filename: Hussain_etal_JPCS_2022_Model_predictive_control_of_wind_turbine_with_aero_elastically_tailored_blades.pdf
Final Published Version
License: Creative Commons Attribution 3.0 logo
Download (1MB)| Preview

Abstract

The use of aero-elastically tailored blades (ATB) for large wind turbines has shown the benefit of mitigating blade loads, in a passive adaptive manner, with the design of bend-twist coupling (BTC) along the blades. The BTC design makes the blades torsionally flexible and capable of adapting to different wind speeds. However, such increased flexibility makes the turbine modeling computationally demanding and the real-time controller design more challenging. In this work, to include the ATB effect into the turbine model for control, a twofold modeling for ATB characteristics is proposed. First a static BTC distribution is added to the turbine aerodynamics to account for the blade’s pre-bend-twist design, next a second order transfer function is introduced to approximate the blade structural dynamic response to wind speed variations. The nonlinear model of the whole ATB wind turbine is built up in Simulink, linearized and discretized into a state-space form. A model predictive controller (MPC) is developed with the actuator constraints considered. Simulation studies are conducted on a 5MW ATB wind turbine at a selected above-rated wind speed. The use of the simplified model for control is assessed and the performance of MPC is compared to the gain-scheduling baseline controller.

CORE (COnnecting REpositories)