Independent Pitch Adaptive Control of Large Wind Turbines Using State Feedback and Disturbance Accommodating Control

Yingming Liu, Yi Wang () and Xiaodong Wang
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Yingming Liu: School of Electrical Engineering, Shenyang University of Technology, Liao Shen West Road 111, Shenyang 110027, China
Yi Wang: School of Electrical Engineering, Shenyang University of Technology, Liao Shen West Road 111, Shenyang 110027, China
Xiaodong Wang: School of Electrical Engineering, Shenyang University of Technology, Liao Shen West Road 111, Shenyang 110027, China

Energies, 2024, vol. 17, issue 18, 1-17

Abstract: Wind turbines experience significant unbalanced loads during operation, exacerbated by external disturbances that challenge the stability of the pitch control system and affect output power. This paper proposes an independent pitch adaptive control strategy integrating state feedback and disturbance accommodating control (DAC). Initially, nonlinear wind turbine dynamics are globally linearized, and DAC is applied to mitigate the impact of wind disturbances dynamically. Subsequently, the entire range of wind speeds is segmented, and controllers are individually designed to optimize gain settings according to specific control objectives at each wind speed interval. Scheduling parameters such as collective pitch angle and tower fore-aft displacement are identified and trained using Radial Basis Function Neural Networks (RBFNN). Finally, based on the output gain values determined by RBFNN, the full-state feedback controller group is adaptively adjusted, and the optimal controller is selected for the final output. Simulations conducted on the NREL 5MW reference wind turbine model using FAST and Simulink demonstrate that compared to the ROSCO controller, the proposed strategy ensures smoother output power and effectively reduces blade and tower loads, thereby extending the turbine’s operational lifespan.

Keywords: wind turbines; independent pitch control; disturbance control; neural network adaptive; status feedback (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: 2024
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