Design of Optimal Pitch Controller for Wind Turbines Based on Back-Propagation Neural Network

Shengsheng Qin (), Zhipeng Cao, Feng Wang, Sze Song Ngu, Lee Chin Kho and Hui Cai
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Shengsheng Qin: School of Electrical Engineering, Yancheng Institute of Technology, Yancheng 224051, China
Zhipeng Cao: School of Electrical Engineering, Yancheng Institute of Technology, Yancheng 224051, China
Feng Wang: School of Electrical Engineering, Yancheng Institute of Technology, Yancheng 224051, China
Sze Song Ngu: Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Malaysia Sarawak, Kota Samarahan 94300, Sarawak, Malaysia
Lee Chin Kho: Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Malaysia Sarawak, Kota Samarahan 94300, Sarawak, Malaysia
Hui Cai: School of Electrical Engineering, Yancheng Institute of Technology, Yancheng 224051, China

Energies, 2024, vol. 17, issue 16, 1-22

Abstract: To ensure the stable operation of a wind turbine generator system when the wind speed exceeds the rated value and address the issue of excessive rotor speed during high wind speeds, this paper proposes a novel variable pitch controller strategy based on a back-propagation neural network and optimal control theory to solve this problem. Firstly, a mathematical model for the wind turbine is established and linearized. Then, each optimal sub-controller is designed for different wind speed conditions by optimal theory. Subsequently, a back-propagation neural network is utilized to learn the variation pattern of controller parameters with respect to wind speed. Finally, real-time changes in wind speed are applied to evaluate and adjust controller parameters using the trained back-propagation neural network. The model is simulated in MATLAB 2019b, real-time data are observed, and the control effect is compared with that of a Takagi–Sugeno optimal controller, firefly algorithm optimal controller and fuzzy controller. The simulation results show that the rotor speed overshoot of the optimal controller under the step wind speed is the smallest, only 0.05 rad/s. Under other wind speed conditions, the rotor speed range fluctuates around 4.35 rad/s, and the fluctuation size is less than 0.2 rad/s, which is much smaller than the fluctuation range of other controllers. It can be seen that the back-propagation optimal controller can ensure the stability of the rotor speed above the rated wind speed. At the same time, it has better control accuracy compared to other controllers.

Keywords: constant power control; optimal controller design; back-propagation neural network; MATLAB 2019b (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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