LAPSE:2018.1038
Published Article
LAPSE:2018.1038
The Study of Fuzzy Proportional Integral Controllers Based on Improved Particle Swarm Optimization for Permanent Magnet Direct Drive Wind Turbine Converters
Yancai Xiao, Tieling Zhang, Zeyu Ding, Chunya Li
November 27, 2018
In order to meet the requirements of high precision and fast response of permanent magnet direct drive (PMDD) wind turbines, this paper proposes a fuzzy proportional integral (PI) controller associated with a new control strategy for wind turbine converters. The purpose of the control strategy is to achieve the global optimization for the quantization factors, ke and kec, and scale factors, kup and kui, of the fuzzy PI controller by an improved particle swarm optimization (PSO) method. Thus the advantages of the rapidity of the improved PSO and the robustness of the fuzzy controller can be fully applied in the control process. By conducting simulations for 2 MW PMDD wind turbines with Matlab/Simulink, the performance of the fuzzy PI controller based on the improved PSO is demonstrated to be obviously better than that of the PI controller or the fuzzy PI controller without using the improved PSO under the situation when the wind speed changes suddenly.
Keywords
converter, fuzzy PI controller, particle swarm optimization (PSO), permanent magnet direct drive (PMDD) wind turbine
Suggested Citation
Xiao Y, Zhang T, Ding Z, Li C. The Study of Fuzzy Proportional Integral Controllers Based on Improved Particle Swarm Optimization for Permanent Magnet Direct Drive Wind Turbine Converters. (2018). LAPSE:2018.1038
Author Affiliations
Xiao Y: School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China
Zhang T: School of Mechanical, Materials and Mechatronic Engineering, University of Wollongong, Northfields Ave, Wollongong, NSW 2522, Australia
Ding Z: School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China
Li C: School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China
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Journal Name
Energies
Volume
9
Issue
5
Article Number
E343
Year
2016
Publication Date
2016-05-06
Published Version
ISSN
1996-1073
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PII: en9050343, Publication Type: Journal Article
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LAPSE:2018.1038
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doi:10.3390/en9050343
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Nov 27, 2018
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Calvin Tsay
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