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Adaptive cooperation control of wind power generation systems based on Hamilton system under limited input.

Authors :
Wu, Zhongqiang
Hou, Lincheng
Source :
International Journal of Adaptive Control & Signal Processing. Nov2023, Vol. 37 Issue 11, p2895-2914. 20p.
Publication Year :
2023

Abstract

Summary: Numerous wind turbines form largeā€scale wind farms, which are complex nonlinear systems with uncertain parameters. The issue of maximum wind energy capture and coordinated control has always been a research hotspot. In this article, under the condition of limited input and uncertain parameters, the preset controller and the adaptive cooperation control are designed to realize the maximum wind energy capture for every wind turbine and the adaptive cooperation control of multiple wind turbines. The research gap lies in that the Hamilton model of wind power generation system is established with uncertain parameters, and the preset controller (method) is designed to capture the maximum wind energy. Under the hypothesis that the uncertain part can be expressed as a linear form about unknown parameter, and using the saturation function processing method in the diagonal matrix, an adaptive feedback controller with limited input is designed to realize the adaptive cooperation control of multiple wind turbines. The simulation results show that under the conditions such as variable wind speed, limited input and uncertain parameters, the wind turbine remain normal operation at the desired angular velocity. It can be concluded that not only the maximum wind energy capture is realized under the condition of variable wind speed, in which the wind turbine can operate on the optimal power curve to improve the utilization of wind energy, but also the adaptive cooperation control of multiple wind turbines can be achieved with limited input and parameter perturbation. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
08906327
Volume :
37
Issue :
11
Database :
Academic Search Index
Journal :
International Journal of Adaptive Control & Signal Processing
Publication Type :
Academic Journal
Accession number :
173439111
Full Text :
https://doi.org/10.1002/acs.3665