An application of recurrent fuzzy neural networks in wind turbine pitch angle control



  • Nguyen Thi Xuan Nhi Vinh Long Vocational College
  • Nguyen Chi Ngon (Corresponding Author) Can Tho University
  • Nguyen Nhut Tien Can Tho University
  • Tran Thanh Luan Vinh Long University of Technology Education



MATLAB simulation; Pitch angel control; Recurrent fuzzy neural network; Wind turbine; Supervisory control.


Nowadays, renewable energy has been developing strongly, including wind energy. However, the use of this energy source is still dependent on natural conditions because the wind intensity changes continuously, making the power generated from the turbine is unstable. That has a huge impact on the electrical system. This paper presents a solution to control and monitor the pitch angle of the wind turbine to generate the rated power aiming to maintain the grid voltage at a stable level. A supervisory controller using recurrent neural fuzzy networks is proposed and tested on MATLAB/Simulink, under the condition of changing wind speeds.


[1]. P. F. Odgaard, J. Stoustrup and M. Kinnaert, “Fault-tolerant control of wind turbines: A benchmark model,” IEEE Trans. Control Syst. Technol, Vol. 21, No. 4, pp. 1168-1182, (2013).

[2]. N. C. Ngon and N. M. Hoang, “Improvement of power output of the wind turbine by pitch angle control using RBF neural network,” International Journal of Mechanical Engineering and Technology, Vol. 10, Issue 10, pp. 64-74, (2019).

[3]. H. Jafarnejadsani, J. Pieper and J. Ehlers, “Adaptive Control of a Variable Speed Variable-Pitch Wind Turbine Using Radial-Basis Function Neural Network,” IEEE Transactions on Control Systems Technology, Vol. 21, No. 6, pp. 2264-2272, (2013).

[4]. A. H. Norouzi and A. M. Sharaf, “Two control schemes to enhance the dynamic performance of the STATCOM and SSSC,” IEEE Trans. on Power Delivery, Vol. 20, No. 1, pp. 435-442, (2005).

[5]. F. D. Bianchi, H. D. Battista and R. J. Mantz, “Wind Turbine Control Systems,” Springer, London, 208 p., ISBN: 978-1-84996-611-5, (2007).

[6]. A. Hwas and R. Katebi, “Wind turbine control using PI pitch angle controller,” IFAC Proceedings Volumes, Vol. 45, Issue 3, pp. 241-246, (2012).

[7]. S. Behera, B. Subudhi and B. B. Pati, “Design of PI controller in pitch control of wind turbine: A comparison of PSO and PS algorithm,” Inter. J. of Renewable Energy Research (IJRER), Vol. 6, pp. 271-281, (2016).

[8]. J. Liu, “Radial Basis Function (RBF) Neural Network Control for Mechanical Systems - Design, Analysis and Matlab Simulation,” Springer Berlin, 365 p., ISBN: 978-3-642-43455-6, (2015).

[9]. N. C. Ngon and D. Tin, “Adaptive single neural PID control based on recurrent fuzzy neural network: An application to ball and beam control system,” Can Tho University Journal of Science, No. 20a, pp. 169-175, (2011).

[10]. L. M. Thanh, L. H. Thuong, P. T. Loc, C. N. Nguyen, “Delta Robot Control Using Single Neuron PID Algorithms Based on Recurrent Fuzzy Neural Network Identifiers,” International Journal of Mechanical Engineering and Robotics Research, Vol. 9, No. 10, pp. 1411-1418, (2020).

[11]. L. M. Thanh, L. H. Thuong, P. T. Tung, and C. N. Nguyen, “Improvement of PID Controllers by Recurrent Fuzzy Neural Networks for Delta Robot,” Springer Singapore, in Intelligent Communication, Control and Devices, pp.263-275, (2021).

[12]. L. C. Hung, T. C. Cheng, “Identification and control of dynamic systems using recurrent fuzzy neural networks,” IEEE Transactions on Fuzzy Systems, Vol. 8, No. 4, pp. 349-366, (2000).

[13]. B. Boukhezzar and H. Siguerdidjane, "Nonlinear Control of Variable Speed Wind Turbines without wind speed measurement," Proc. of the 44th IEEE Conference on Decision and Control, pp. 3456-3461, doi: 10.1109/CDC.2005.1582697, (2005).

[14]. G. Abad, J. Lopez, M. Rodriguez, L. Marroyo and G. Iwanski, “Doubly fed induction machine: modeling and control for wind energy generation,” Wiley-IEEE Press, 625 p., ISBN: 978-0-470-76865-5, (2011).



How to Cite

Nguyễn Thị Xuân Nhi, C. N. Nguyen, Nguyễn Nhựt Tiến, and Trần Thành Luân. “An Application of Recurrent Fuzzy Neural Networks in Wind Turbine Pitch Angle Control”. Journal of Military Science and Technology, no. 80, June 2022, pp. 3-12, doi:10.54939/1859-1043.j.mst.80.2022.3-12.



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