Improved control strategy and designed control parameters of pitch system for wind turbine considering blade load reduction

Shuyuan Zhang, Ying Wang, Yingming Liu, Xiaodong Wang, Wenchao Bai and Tian Cao

Renewable Energy, 2024, vol. 232, issue C

Abstract: As wind turbines increase in the power, the longer the blade, the more severe the blade load. Traditional control strategies and control parameters do not consider to reduce blade load, which seriously threatens the safe operation of wind turbines. Therefore, this study proposes a pitch control strategy based on blade active damping control and a multistage control parameter design method. Firstly, this study proposes a pitch control strategy based on blade active damping control to reduce the blade load based on the establishment of a mathematical model of the pitch system considering the blade load and a dynamic model of blade active damping control. Secondly, in the control parameter initial values design of Stage Ⅰ, this study proposes a variable forgetting factor recursive least squares to identify a simplified mathematical model of the pitch system and a Low–overshoot Chien–Hrones–Reswisk to solve the proportional integral control parameter initial values. Thirdly, in the control parameter optimal values design of Stage Ⅱ, this study proposes an adaptive genetic algorithm with improved hyperbolic tangent function to optimize control parameter at the equilibrium points and proposes an adaptive control to calculate control parameter at the non–equilibrium points. Finally, based on the Bladed software for 2 MW and 5 MW wind turbines, the superiority of the proposed methods, effectiveness and applicability in improving the control accuracy of the output power and generator speed, and reducing the load situation at the blade root position are verified.

Keywords: Wind turbine; Pitch system; Control strategy; Control parameter (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:232:y:2024:i:c:s0960148124011182

DOI: 10.1016/j.renene.2024.121050

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