single-jc.php

JACIII Vol.20 No.2 pp. 332-341
doi: 10.20965/jaciii.2016.p0332
(2016)

Paper:

Research on Fuzzy PID Pitch-Controlled System Based on SVM

Huijun Yu*,**, Yong He***, Zhengli Zhao**, and Min Wu***

*School of Information Science and Engineering, Central South University
Changsha 410083, China

**School of Electrical and Information Engineering, Hunan University of Technology
Zhuzhou 412007, China

***School of Automation, China University of Geosciences
Wuhan 430074, China

Received:
November 10, 2015
Accepted:
December 10, 2015
Online released:
March 18, 2016
Published:
March 20, 2016
Keywords:
wind power generation system, variable pitch, fuzzy PID control, SVM
Abstract

In a wind turbine generator, it is difficult to achieve a good control performance using the conventional PID variable pitch controller because of the multi-variables and nonlinearities of the controller. Based on the principles of variable pitch control of a wind generating set, this study introduces a mathematical model for wind turbine generators and proposes a variable pitch fuzzy PID control method by combining the fuzzy control and PID control methods to effectively overcome the deficiencies of the conventional PID controller. In order to enhance self-learning and predictability of the system, an improved method for the variable pitch control of the fuzzy PID is proposed based on an online support vector machine (SVM). Finally, the simulation comparisons show that the fuzzy PID variable pitch control based on the SVM further improves the control performance of a variable pitch wind turbine generator. In addition, it is robust at random wind speeds and can adjust the pitch angle more smoothly. It ensures a stable output power of wind turbine generator, thus effectively improving the control performance of the variable pitch system of the wind generating set.

References
  1. [1] E. Adzic, Z. Ivanovic, M. Adzic, and V. Katic, “Maximum power search in wind turbine based on fuzzy logic control,” Acta Polytechnica Hungarica, Vol.6, No.1, pp. 131-149, 2009.
  2. [2] X. Song and B. Liang, “Wind power system pitch control based on fuzzy self-learning emendation control theory,” Power System Protection and Control, Vol.37, No.16, pp. 50-58, 2009.
  3. [3] H. Camblong, “Digital robust control of a variable speed pitch regulated wind turbine for above rated wind speeds,” Control Engineering Practice, Vol.16, No.8, pp. 946958, 2008.
  4. [4] A. S. Yilmaz and Z. özer, “Pitch Angle Control in Wind Turbines Above the Rated Wind Speed by Multi-Layer Perception and Radial Basis Function Neural Networks,” Expert Systems with Applications, Vol.36, No.6, pp. 9767-9775, 2009.
  5. [5] P. Novak, T. Ekelund, I. Jovik, and B. Schmidtbauer, “Modeling and control of variable-speed wind turbine drive-system dynamics,” IEEE Control Systems, Vol.15, No.1, pp. 28-37, 1995.
  6. [6] W. Chen, D. J. Balance, and P. J. Gawthrop, “Optimal control of nonlinear systems: a predictive control approach,” Automatica, Vol.39, No.4, pp. 633-641, 2003.
  7. [7] S. E. Woodard and D. P. Garg, “A numerical optimization approach for tuning fuzzy logic controllers,” IEEE Trans. on Systems, Man and Cybernetics, Part B: Cybernetics, Vol.29, No.4, pp. 565-569, 1999.
  8. [8] Y. Yin, S. Fan and M. Chen, “The design and simulation of adaptive fuzzy PID controller,” Fire Control and Command Control, Vol.33, No.7, pp. 96-99, 2008.
  9. [9] J. Carvajal, G. Chen and H. Ogmen, “Fuzzy PID controller: Design, performance evaluation, and stability analysis,” Information Sciences, Vol.123, No.3, pp. 249-270, 2000.
  10. [10] S. K. Shevade, S. S. Keerthi, C. Bhattacharyya and K. R. K. Murthy, “Improvements to the SMO algorithm for SVM regression,” IEEE Trans. on Neural Networks, Vol.11, No.5, pp. 1188-1193, 2000.
  11. [11] G. Peng, “Variable pitch control of wind turbine generator combined with fuzzy feed forward and fuzzy PID controller,” Proc. of the CSEE, Vol.30, No.8, pp. 123-128.
  12. [12] X. Yuan, Y. Wang, and H. Yang, “Support vector machines based nonlinear inverse control and simulation,” J. of Hunan University (Natural Sciences), Vol.33, No.1, pp. 71-74, 2006.
  13. [13] D. H. Hong and C. Hwang, “Support vector fuzzy regression machines,” Fuzzy Sets and Systems, Vol.138, No.2, pp. 271-281, 2003.
  14. [14] V. Cherkassky and Y. Ma, “Practical selection of SVM parameters and noise estimation for SVM regression,” Neural Networks, Vol.17, No.1, pp. 113-126, 2004.
  15. [15] M. E. Mavroforakis and S. Theodoridis, “A geometric approach to support vector machine (SVM) classification,” IEEE Trans. on Neural Networks, Vol.17, No.3, pp. 671-682, 2006.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Oct. 24, 2017