Design and Assessment of a LIDAR-Based Model Predictive Wind Turbine Control
Jie Bao and
Hong Yue ()
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Jie Bao: Wind Energy and Control Centre, Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK
Hong Yue: Wind Energy and Control Centre, Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK
Energies, 2022, vol. 15, issue 17, 1-19
Abstract:
The development of the Light Detection and Ranging (LIDAR) technology has enabled wider options for wind turbine control, in particular regarding disturbance rejection. The LIDAR measurements provide a spatial, preview wind information, based on which the controller has a better chance to cope with the wind disturbance before it affects the turbine operation. In this paper, a model predictive controller for above-rated wind turbine control was developed with the use of pseudo-LIDAR wind measurements data. A predictive control algorithm was developed based on a linearised wind turbine model, in which the disturbance from the incoming wind was computed by the LIDAR simulator. The optimal control action was applied to the nonlinear turbine model. The developed controller was compared with the baseline control and a previously developed LIDAR-assisted control combining a feedback-and-feedforward design. Our simulation studies on a 5 MW nonlinear wind turbine model, under different wind conditions, demonstrated that the developed LIDAR-based predictive control achieved improved performance in the presence of small variations in the out-of-plane rotor torque and fore-aft tower acceleration, as well as a smoother generator speed regulation and satisfied pitch activity control constraints.
Keywords: wind turbine control; LIDAR wind information; model predictive control (MPC) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:17:p:6429-:d:905571
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