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Design of Optimal Pitch Controller for Wind Turbines Based on Back-Propagation Neural Network

To ensure the stable operation of a wind turbine generator system when the wind speed exceeds the rated value and address the issue of excessive rotor speed during high wind speeds, this paper proposes a novel variable pitch controller strategy based on a back-propagation neural network and optimal...

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Main Authors: Shengsheng, Qin, Zhipeng, Cao, Feng, Wang, Ngu, Sze Song, Kho, Lee Chin, Hui, Cai
Format: Article
Language:English
Published: MDPI 2024
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://ir.unimas.my/id/eprint/46214/1/energies-17-04076.pdf
http://ir.unimas.my/id/eprint/46214/
https://www.mdpi.com/1996-1073/17/16/4076
https://doi.org/10.3390/en17164076
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spelling my.unimas.ir.462142024-10-03T06:23:32Z http://ir.unimas.my/id/eprint/46214/ Design of Optimal Pitch Controller for Wind Turbines Based on Back-Propagation Neural Network Shengsheng, Qin Zhipeng, Cao Feng, Wang Ngu, Sze Song Kho, Lee Chin Hui, Cai TK Electrical engineering. Electronics Nuclear engineering To ensure the stable operation of a wind turbine generator system when the wind speed exceeds the rated value and address the issue of excessive rotor speed during high wind speeds, this paper proposes a novel variable pitch controller strategy based on a back-propagation neural network and optimal control theory to solve this problem. Firstly, a mathematical model for the wind turbine is established and linearized. Then, each optimal sub-controller is designed for different wind speed conditions by optimal theory. Subsequently, a back-propagation neural network is utilized to learn the variation pattern of controller parameters with respect to wind speed. Finally, real-time changes in wind speed are applied to evaluate and adjust controller parameters using the trained back-propagation neural network. The model is simulated in MATLAB 2019b, real-time data are observed, and the control effect is compared with that of a Takagi–Sugeno optimal controller, firefly algorithm optimal controller and fuzzy controller. The simulation results show that the rotor speed overshoot of the optimal controller under the step wind speed is the smallest, only 0.05 rad/s. Under other wind speed conditions, the rotor speed range fluctuates around 4.35 rad/s, and the fluctuation size is less than 0.2 rad/s, which is much smaller than the fluctuation range of other controllers. It can be seen that the back-propagation optimal controller can ensure the stability of the rotor speed above the rated wind speed. At the same time, it has better control accuracy compared to other controllers. MDPI 2024 Article PeerReviewed text en http://ir.unimas.my/id/eprint/46214/1/energies-17-04076.pdf Shengsheng, Qin and Zhipeng, Cao and Feng, Wang and Ngu, Sze Song and Kho, Lee Chin and Hui, Cai (2024) Design of Optimal Pitch Controller for Wind Turbines Based on Back-Propagation Neural Network. Energies, 17 (16). pp. 1-22. ISSN 1996-1073 https://www.mdpi.com/1996-1073/17/16/4076 https://doi.org/10.3390/en17164076
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Shengsheng, Qin
Zhipeng, Cao
Feng, Wang
Ngu, Sze Song
Kho, Lee Chin
Hui, Cai
Design of Optimal Pitch Controller for Wind Turbines Based on Back-Propagation Neural Network
description To ensure the stable operation of a wind turbine generator system when the wind speed exceeds the rated value and address the issue of excessive rotor speed during high wind speeds, this paper proposes a novel variable pitch controller strategy based on a back-propagation neural network and optimal control theory to solve this problem. Firstly, a mathematical model for the wind turbine is established and linearized. Then, each optimal sub-controller is designed for different wind speed conditions by optimal theory. Subsequently, a back-propagation neural network is utilized to learn the variation pattern of controller parameters with respect to wind speed. Finally, real-time changes in wind speed are applied to evaluate and adjust controller parameters using the trained back-propagation neural network. The model is simulated in MATLAB 2019b, real-time data are observed, and the control effect is compared with that of a Takagi–Sugeno optimal controller, firefly algorithm optimal controller and fuzzy controller. The simulation results show that the rotor speed overshoot of the optimal controller under the step wind speed is the smallest, only 0.05 rad/s. Under other wind speed conditions, the rotor speed range fluctuates around 4.35 rad/s, and the fluctuation size is less than 0.2 rad/s, which is much smaller than the fluctuation range of other controllers. It can be seen that the back-propagation optimal controller can ensure the stability of the rotor speed above the rated wind speed. At the same time, it has better control accuracy compared to other controllers.
format Article
author Shengsheng, Qin
Zhipeng, Cao
Feng, Wang
Ngu, Sze Song
Kho, Lee Chin
Hui, Cai
author_facet Shengsheng, Qin
Zhipeng, Cao
Feng, Wang
Ngu, Sze Song
Kho, Lee Chin
Hui, Cai
author_sort Shengsheng, Qin
title Design of Optimal Pitch Controller for Wind Turbines Based on Back-Propagation Neural Network
title_short Design of Optimal Pitch Controller for Wind Turbines Based on Back-Propagation Neural Network
title_full Design of Optimal Pitch Controller for Wind Turbines Based on Back-Propagation Neural Network
title_fullStr Design of Optimal Pitch Controller for Wind Turbines Based on Back-Propagation Neural Network
title_full_unstemmed Design of Optimal Pitch Controller for Wind Turbines Based on Back-Propagation Neural Network
title_sort design of optimal pitch controller for wind turbines based on back-propagation neural network
publisher MDPI
publishDate 2024
url http://ir.unimas.my/id/eprint/46214/1/energies-17-04076.pdf
http://ir.unimas.my/id/eprint/46214/
https://www.mdpi.com/1996-1073/17/16/4076
https://doi.org/10.3390/en17164076
_version_ 1812131556265820160
score 13.211869

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