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A modified Adaptive Wavelet PID Control Based on Reinforcement Learning for Wind Energy Conversion System ControlSEDIGHIZADEH, M.   , REZAZADEH, A. |
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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
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. |
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[1] PID Neural Network Based Speed Control of Asynchronous Motor using Programmable Logic Controller, MARABA, V. A., KUZUCUOGLU, A. E., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 4, Volume 11, 2011.
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[2] Adaptive T–S fuzzy controller using reinforcement learning based on Lyapunov stability, Khater, A. Aziz, El-Nagar, Ahmad M., El-Bardini, Mohammad, El-Rabaie, Nabila M., Journal of the Franklin Institute, ISSN 0016-0032, Issue 14, Volume 355, 2018.
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[3] A Novel Structure of Actor-Critic Learning Based on an Interval Type-2 TSK Fuzzy Neural Network, Khater, A. Aziz, El-Nagar, Ahmad M., El-Bardini, Mohammad, El-Rabaie, Nabila, IEEE Transactions on Fuzzy Systems, ISSN 1063-6706, Issue 11, Volume 28, 2020.
Digital Object Identifier: 10.1109/TFUZZ.2019.2949554 [CrossRef]
[4] $H_{\infty }$ PID Control for Discrete-Time Fuzzy Systems With Infinite-Distributed Delays Under Round-Robin Communication Protocol, Wang, Yezheng, Wang, Zidong, Zou, Lei, Dong, Hongli, IEEE Transactions on Fuzzy Systems, ISSN 1063-6706, Issue 6, Volume 30, 2022.
Digital Object Identifier: 10.1109/TFUZZ.2021.3069329 [CrossRef]
[5] Reinforcement learning in wind energy - a review, Narayanan, Valayapathy Lakshmi, International Journal of Green Energy, ISSN 1543-5075, Issue 9, Volume 21, 2024.
Digital Object Identifier: 10.1080/15435075.2023.2281329 [CrossRef]
[6] Design of Deep Reinforcement Learning Controller Through Data-assisted Model for Robotic Fish Speed Tracking, Duraisamy, Palmani, Nagarajan Santhanakrishnan, Manigandan, Rengarajan, Amirtharajan, Journal of Bionic Engineering, ISSN 1672-6529, Issue 3, Volume 20, 2023.
Digital Object Identifier: 10.1007/s42235-022-00309-7 [CrossRef]
[7] Pitch Based Wind Turbine Intelligent Speed Setpoint Adjustment Algorithms, González-González, Asier, Etxeberria-Agiriano, Ismael, Zulueta, Ekaitz, Oterino-Echavarri, Fernando, Lopez-Guede, Jose, Energies, ISSN 1996-1073, Issue 6, Volume 7, 2014.
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Digital Object Identifier: 10.4316/AECE.2012.04005 [CrossRef] [Full text]
[9] Model predictive control for constrained robot manipulator visual servoing tuned by reinforcement learning, Li, Jiashuai, Peng, Xiuyan, Li, Bing, Sreeram, Victor, Wu, Jiawei, Chen, Ziang, Li, Mingze, Mathematical Biosciences and Engineering, ISSN 1551-0018, Issue 6, Volume 20, 2023.
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[10] Improving the performance of a class of adaptive fuzzy controller based on stable and fast on-line learning algorithm, Khater, Abdel Aziz, El-Nagar, Ahmad M., El-Bardini, Mohammad, El-Rabaie, Nabila M., European Journal of Control, ISSN 0947-3580, Issue , 2020.
Digital Object Identifier: 10.1016/j.ejcon.2019.07.001 [CrossRef]
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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.
