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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
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ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  2/2010 - 27
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A modified Adaptive Wavelet PID Control Based on Reinforcement Learning for Wind Energy Conversion System Control

SEDIGHIZADEH, M. See more information about SEDIGHIZADEH, M. on SCOPUS See more information about SEDIGHIZADEH, M. on IEEExplore See more information about SEDIGHIZADEH, M. on Web of Science, REZAZADEH, A. See more information about REZAZADEH, A. on SCOPUS See more information about REZAZADEH, A. on SCOPUS See more information about REZAZADEH, A. on Web of Science
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,587 KB) | Citation | Downloads: 1,668 | Views: 3,359

Author keywords
control, reinforcement, neural network, wavelet, wind energy

References keywords
control(14), wind(10), adaptive(8), neural(7), networks(7), sedighizadeh(6), turbine(5), systems(5), energy(5), conversion(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2010-05-31
Volume 10, Issue 2, Year 2010, On page(s): 153 - 159
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2010.02027
Web of Science Accession Number: 000280312600027
SCOPUS ID: 77954629341

Abstract
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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 modified self-tuning PID control strategy, using reinforcement learning for WECS control. The controller employs Actor-Critic learning in order to tune PID parameters adaptively. These Actor-Critic learning is a special kind of reinforcement learning that uses a single wavelet neural network to approximate the policy function of the Actor and the value function of the Critic simultaneously. These controllers are used to control a typical WECS in noiseless and noisy condition and results are compared with an adaptive Radial Basis Function (RBF) PID control based on reinforcement learning and conventional PID control. Practical emulated results prove the capability and the robustness of the suggested controller versus the other PID controllers to control of the WECS. The ability of presented controller is tested by experimental setup.


References | Cited By  «-- Click to see who has cited this paper

[1] Astrom, K. J., Wittenmark, B., "Adaptive Control", Addison-Wesley, New York, 1995.

[2] Leva, A., "PID autotuning algorithm based on relay feedback", IEE Proceedings-D 140 (5) pp. 328-337, 1993.

[3] Chedid, R., Mrad, F., Basman, M., "Intelligent control of a class of wind energy conversion system", IEEE T-EC 14(4):1597-1604, 1999.

[4] Kanellos, F. D., Hatziargyriou, N. D., "A new control scheme for variable speed wind turbine using neural networks", IEEE Power Eng Soc Winter Meeting 1(1):360-365, 2002.

[5] Kyoungsoo, Ro, Han-ho, Choi, "Application of neural network controller for maximum power extraction of a grid-connected wind turbine system", Electr Eng (Archiv Elektrotech) 88(1):45-53, 2005.
[CrossRef] [Web of Science Times Cited 15] [SCOPUS Times Cited 35]


[6] Narendra. K. S., Parthasarathy K., "Identification and control of dynmical systems using neural networks," IEEE Trans. On neural networks, Vol. 1, No. 1, pp. 4-27, 1990.
[CrossRef] [PubMed] [SCOPUS Times Cited 4876]


[7] Mayosky, M. A., Cancelo, G. I. E., "Direct adaptive control of wind energy conversion systems using gaussian networks," IEEE Transactions on neural networks, Vol. 10, No.4, pp. 898-906, 1999.
[CrossRef] [PubMed] [Web of Science Times Cited 43] [SCOPUS Times Cited 60]


[8] Kalantar, M., Sedighizadeh, M., "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, 2004

[9] Sedighizadeh, M., Kalantar, M., "Adaptive PID control of wind energy conversion systems using RASP1 mother wavelet basis function networks", IEEE TENCON 2004, Chiang Mai, 2004.
[CrossRef]


[10] Sedighizadeh, M., et al., "Nonlinear model identification and control of wind turbine using wavenets", Proceedings of the 2005 IEEE conference on control applications, Toronto, pp. 1057-1062, 2005.
[CrossRef]


[11] Sedighizadeh, M., Rezazadeh, A., "Self Tuning Control of Wind Turbine Using Neural Network Identifier", Electr Eng (Archiv Elektrotech). Volume 90, Number 7, Sept. 2008, pp. 479-491, 2008.

[12] Sedighizadeh, M., Rezazadeh, A., "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, ISSN 1307-6884, pp.257-262, 2008.

[13] Wang, X. S., Cheng, Y. H., Sun W., "A Proposal of Adaptive PID Controller Based on Reinforcement Learning", J China Univ Mining & Technol. 17(1): 0040-0044, 2007.

[14] Sedighizadeh, M., Rezazadeh, A., Khatibi, M., "A self-tuning PID control for a wind energy conversion system based on the Lyapunov approach", 43rd International Universities Power Engineering Conference, 2008. UPEC 2008, 1-4 Sept. 2008, pp. 1-4,
[CrossRef] [SCOPUS Times Cited 5]


[15] Wang, X. S., Cheng, Y. H., Sun, W. "Q learning based on self-organizing fuzzy radial basis function network", Lecture Notes in Computer Science, 3971: 607-615., 2006.
[CrossRef]


[16] Barto, A. G., Sutton, R. S., Anderson, C. W., "Neuron like adaptive elements that can solve difficult learning control problems", IEEE Transactions on Systems, Man and Cybernetics 13(5), pp834-846, 1983.

[17] Szu, H. H., Telfer, B. A., Kadambe, S., "Neural network adaptive wavelets for signal representation and classification", Opt Eng 31(9):1907-1916, 1992.
[CrossRef] [Web of Science Times Cited 188]


References Weight

Web of Science® Citations for all references: 246 TCR
SCOPUS® Citations for all references: 4,976 TCR

Web of Science® Average Citations per reference: 14 ACR
SCOPUS® Average Citations per reference: 293 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2017-08-16 08:58 in 58 seconds.




Note1: Web of Science® is a registered trademark of Thomson Reuters.
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Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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