|2/2010 - 27|
A modified Adaptive Wavelet PID Control Based on Reinforcement Learning for Wind Energy Conversion System ControlSEDIGHIZADEH, M. , REZAZADEH, A.
|Click to see author's profile on SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (1,587 KB) | Citation | Downloads: 1,671 | Views: 3,448|
control, reinforcement, neural network, wavelet, wind energy
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
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|
| Astrom, K. J., Wittenmark, B., "Adaptive Control", Addison-Wesley, New York, 1995.
 Leva, A., "PID autotuning algorithm based on relay feedback", IEE Proceedings-D 140 (5) pp. 328-337, 1993.
 Chedid, R., Mrad, F., Basman, M., "Intelligent control of a class of wind energy conversion system", IEEE T-EC 14(4):1597-1604, 1999.
 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.
 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 36]
 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 4923]
 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 44] [SCOPUS Times Cited 63]
 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
 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.
 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.
 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.
 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.
 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.
 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]
 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.
 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.
 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 189]
Web of Science® Citations for all references: 248 TCR
SCOPUS® Citations for all references: 5,027 TCR
Web of Science® Average Citations per reference: 15 ACR
SCOPUS® Average Citations per reference: 296 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 2017-10-17 15:59 in 54 seconds.
Note1: Web of Science® is a registered trademark of Thomson Reuters.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
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.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.