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JRM Vol.28 No.6 pp. 921-927
doi: 10.20965/jrm.2016.p0921
(2016)

Paper:

Adaptive Integral Sliding Mode Control via Fuzzy Logic for Variable Speed Wind Turbines

Yan Ren*1,*2,*3, Chuanli Gong*3, Dekuan Wang*3, and Dianwei Qian*4

*1China Three Gorges Corporation
No.1 Yuyuantan South Road, Haidian District, Beijing 100038, China

*2North China University of Water Resources and Electric Power
No.1 Jinshui East Road, Zhengzhou 450045, China

*3China Institute of Water Resources and Hydropower Research
A-1, Fuxing Road, Haidian District, Beijing 100038, China

*4School of Control and Computer Engineering, North China Electric Power University
No.2 Beinong Road, Changping District, Beijing 102206, China

Received:
April 24, 2016
Accepted:
October 11, 2016
Published:
December 20, 2016
Keywords:
sliding mode control, fuzzy logic, wind turbine, uncertainty, linearization
Abstract

Adaptive Integral Sliding Mode Control via Fuzzy Logic for Variable Speed Wind Turbines

Schematic of a wind turbine

Concerning variable speed wind turbines, this study suggests a control scheme that combines integral sliding mode control (I-SMC) and fuzzy logic. The control task is to maintain the output power at the rated value for variable operating points. Wind turbines suffer from serious nonlinearities that challenge the control task. To attack the issue, the nonlinear turbine model is linearized at some typical operating points. Then, pitch-angle and generator-torque controllers based on the linearized turbine models are formulated by the I-SMC approach. Meanwhile, a fuzzy inference system is designed to weight those controllers. Not only the scheme can stabilize nonlinear wind turbines, but also the control system is robust to resist wind-speed variations. Some results are presented to show the performance of the control scheme.

Cite this article as:
Y. Ren, C. Gong, D. Wang, and D. Qian, “Adaptive Integral Sliding Mode Control via Fuzzy Logic for Variable Speed Wind Turbines,” J. Robot. Mechatron., Vol.28, No.6, pp. 921-927, 2016.
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References
  1. [1] G. Gu, R. Hu, and Y. Li, “Study on identification of damage to wind turbine blade based on support vector machine and particle swarm optimization,” J. Robot. Mechatron., Vol.27, No.3, pp. 244-249, 2015.
  2. [2] D. Johnson and R. Erhardt, “Projected impacts of climate change on wind energy density in the United States,” Renew. Energy, Vol.85, pp. 66-73, 2016.
  3. [3] H. Li and Z. Chen, “Overview of different wind generator systems and their comparisons,” IET Renew. Power Gener., Vol.2, No.2, pp. 123-138, 2008.
  4. [4] M. Ma, H. Chen, X. Liu, and F. Allgöwer, “Moving horizon H-infinity control of variable speed wind turbines with actuator saturation,” IET Renew. Power Gener., Vol.8, No.5, pp. 498-508, 2014.
  5. [5] L. Xu and Y. Wang, “Dynamic modeling and control of DFIG-based wind turbines under, unbalanced network conditions,” IEEE Trans. Power Syst., Vol.22, No.1, pp. 314-323, 2007.
  6. [6] A. Lasheen and A. Elshafei, “Wind-turbine collective-pitch control via a fuzzy predictive algorithm,” Renew. Energy, Vol.87, pp. 298-306, 2016.
  7. [7] H. Camblong, S. Nourdine, I. Vechiu, and G. Tapia, “Comparison of an island wind turbine collective and individual pitch LQG controllers designed to alleviate fatigue loads,” IET Renew. Power Gener., Vol.6, No.4, pp. 267-275, 2012.
  8. [8] Z. Yang, Y. Li, and J. Seem, “Multi-model predictive control for wind turbine operation under meandering wake of upstream turbines,” Control Eng. Practice, Vol.45, pp. 37-45, 2015.
  9. [9] C. Caruana, A. Al-Durra, and F. Blaabjerg, “Observer-based scheme for tuning the control of variable speed wind turbines operating in hostile environments,” IET Renew. Power Gener., Vol.10, No.3, pp. 418-425, 2016.
  10. [10] H. Moradi and G. Vossoughi, “Robust control of the variable speed wind turbines in the presence of uncertainties: A comparison between H-infinity and PID controllers,” Energy, Vol.90, pp. 1508-1521, 2015.
  11. [11] M. Seker, E. Zergeroglu, and E. Tatlicioglu, “Non-linear control of variable-speed wind turbines with permanent magnet synchronous generators: a robust backstepping approach,” Int. J. Syst. Sci., Vol.47, No.2, pp. 420-432, 2016
  12. [12] D. Jena and S. Rajendran, “A review of estimation of effective wind speed based control of wind turbines,” Renew. Sust. Energ. Rev., Vol.43, pp. 1046-1062, 2015.
  13. [13] D. Wang, F. Li, S. Wen, X. Qi, P. Liu, and M. Deng, “Operator-based sliding-mode nonlinear control design for a process with input constraint,” J. Robot. Mechatron., Vol.27, No.1, pp. 83-90, 2015.
  14. [14] R. Akbar, B. Sumantri, H. Katayama, S. Sano, and N. Uchiyama, “Reduced-order observer based sliding mode control for a quad-rotor helicopter,” J. Robot. Mechatron., Vol.28, No.3, pp. 304-313, 2016.
  15. [15] D. Li and H. Gutierrez, “Quasi-sliding mode control of a high-precision hybrid magnetic suspension actuator,” J. Robot. Mechatron., Vol.25, No.1, pp. 192-200, 2013.
  16. [16] S. Boksuwan, T. Benjanarasuth, C. Kanamorim, and H. Aoyama, “Robust hybrid control for two-dimensional handheld micromanipulator,” J. Robot. Mechatron., Vol.26, No.3, pp. 331-340, 2014.
  17. [17] B. Beltran, T. Ahmed-Ali, and M. E. H. Benbouzid, “Sliding mode power control of variable-speed wind energy conversion systems,” IEEE Trans. Energy Convers., Vol.23, No.2, pp. 551-558, 2008.
  18. [18] C. Evangelista, P. Puleston, F. Valenciaga, and L. M. Fridman, “Lyapunov-Designed super-twisting sliding mode control for wind energy conversion optimization,” IEEE Trans. Ind. Electron., Vol.60, No.2, pp. 538-545, 2013.
  19. [19] C. Evangelista, F. Valenciaga, and P. Puleston, “Active and reactive power control for wind turbine based on a MIMO-sliding mode algorithm with variable gains,” IEEE Trans. Energy Convers., Vol.28, No.3, pp. 682-689, 2013.
  20. [20] M. Deng and N. Bu, “Robust control for nonlinear systems using passivity-based robust right coprime factorization,” IEEE Trans. Autom. Control, Vol.57, No.10, pp. 2599-2604, 2012.
  21. [21] C. Li, J. Yi and G. Zhang, “On the monotonicity of interval type-2 fuzzy logic systems,” IEEE Trans. Fuzzy Syst., Vol.22, No.5, pp. 1197-1212, 2014.
  22. [22] H. Yoshida, M. Omae, and T. Wada, “Toward next active safety technology of intelligent vehicle,” J. Robot. Mechatron., Vol.27, No.6, pp. 610-616, 2015.
  23. [23] D. Qian, S. Tong, and S. Lee, “Fuzzy-Logic-based control of payloads subjected to double-pendulum motion in overhead cranes,” Autom. Constr., Vol.65, pp. 133-143, 2016.
  24. [24] M. Corradini and G. Orlando, “Control of mobile robots with uncertainties in the dynamical model: a discrete time sliding mode approach with experimental results,” Contr. Eng. Pract., Vol.10, No.1, pp. 23-34, 2002.

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Last updated on Nov. 20, 2018