Lyapunov Based Self-Tuning Control of Wind Energy Conversion System

Seyed Vali Heidari; Mostafa Sedighizadeh; Alireza Rezazade; M. Ahmadzadeh

doi:10.15866/irecon.v1i1.653

Lyapunov Based Self-Tuning Control of Wind Energy Conversion System

Seyed Vali Heidari^(1*), Mostafa Sedighizadeh⁽²⁾, Alireza Rezazade⁽³⁾, M. Ahmadzadeh⁽⁴⁾

^(*) Corresponding author

DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)

Abstract

Nonlinear characteristics of wind turbines and electric generators necessitate complicated and nonlinear control of grid connected Wind Energy Conversion Systems (WECS). This paper proposes a Lyapunov based self-tuning Proportional-Integral- Derivative (PID) control approach for WECS control. This self-tuning PID control is founded on the combination of two controllers, i.e. a self-tuning PID controller, which drives the tracking error to zero with user specified dynamics, and a supervisory controller, based on crude bounds of the system’s nonlinearities. The supervisory controller guarantees the stability of closed-loop nonlinear PID control system. The form of the supervisory control and the adaptation law are derived from a Lyapunov based stability analysis. The results are applied to a typical WECS, presenting the ability of the proposed method
Copyright © 2013 Praise Worthy Prize - All rights reserved.

Full Text:

PDF

References

Astrom , K.J. and B. Wittenmark, 1995. Adaptive control. 2nd edn., Addison-Wesley.

Leva, A., 1993. PID autotuning algorithm based on relay feedback. IEE Proceedings-D. Control Theory and Applications. 140. 5: 328–333.

Mayosky, M. A. and G. I. E. Cancelo,1999. Direct adaptive control of wind energy conversion systems using Gaussian networks. IEEE Transactions on neural networks, 10. 4:898-906.

Narendra, K. S. and K. Parthasarathy, 1990. Identification and control of dynamical systems using neural networks. IEEE Transaction on neural networks, 1. 1. : 4-27.

Sedighizadeh, M. , D. Arzaghi-Harris and M. Kalantar, 2004. Adaptive PID Control of Wind Energy Conversion Systems Using RASP1 Mother Wavelet Basis Function Networks. IEEE TENCON 2004, Chiang Mai, Thailand, 21-24 Nov. 2004., 524- 527

Sedighizadeh, M. M. Kalantar, S. Esfandeh and D. Arzaghi-Harris, 2005. Nonlinear Model Identification and Control of Wind Turbine Using Wavenets. Proceedings of the 2005 IEEE Conference on Control Applications Toronto, Canada, 28-31 Aug. 2005, 1057-1062.

Sedighizadeh, M. A.Rezazadeh, 2008 .Self Tuning Control of Wind Turbine Using Neural Network Identifier. Electrical Engineering (Archiv fur Elektrotechnik), 90, 7:479-491

Tsay , D.L. , H.Y. Chung and C.J. Lee, 1999.The adaptive control of nonlinear systems using the Sugeno-type of fuzzy logic. IEEE Transactions on Fuzzy Systems 7, 2: 225–229.

Kalantar M, Sedighizadeh M (2004) Adaptive self tuning control of wind energy conversion systems using Morlet mother wavelet basis functions networks. In: 12thMediterranean IEEE conference on control and automation MED’04, Kusadasi.

M. Sedighizadeh, A.Rezazadeh, (2008) Adaptive PID Controller based on Reinforcement Learning for Wind Turbine Control. Proceedings of World Academy of Science, Engineering and Technology (CESSE2008), Cairo, Egypt, Vol. 27 , February 2008, ISSN 1307-6884, PP.257-262.

Wang XS, Cheng YH, Sun W (2007). A Proposal of Adaptive PID Controller Based on Reinforcement Learning. J China Univ Mining & Technol .17(1): 0040–0044.

Kanellos FD, Hatziargyriou ND (2002) A new control scheme for variable speed wind turbine using neural networks. IEEE Power Eng Soc Winter Meeting 1(1):360–365.

Kyoungsoo Ro,Han-ho Choi (2005) Application of neural network controller for maximum power extraction of a grid-connected wind turbine system. Electr Eng (Archiv Elektrotech) 88(1):45–53.

Refbacks

There are currently no refbacks.

Username
Password
Remember me