DocumentCode :
2830164
Title :
Neural Network-Based Fuzzy Predictive Current Control for Doubly Fed Machine
Author :
Shao, Zongkai ; Zhan, Yuedong
Author_Institution :
Sch. of Hydropower & Inf. Eng., Huazhong Univ. of Sci. & Technol., Wuhan, China
fYear :
2009
fDate :
11-12 July 2009
Firstpage :
70
Lastpage :
73
Abstract :
In this paper, based on the radial basis function (RBF) neural network, a fuzzy predictive current control strategy for the doubly fed machine (DFM) is presented. The dynamic model of voltage, flux linkage, electromagnetic torque and mechanical motion equation for DFM are expressed. Because the DFM structure is complex and the DFM parameters are variable according to the operating conditions and environments, in order to improve the dynamic performances of DFM, the RBF neural network and fuzzy predictive control theories are employed to design the current controller in the DFM adjustable speed system. Simulation results show effectiveness of the proposed control strategy.
Keywords :
asynchronous machines; electric current control; fuzzy control; machine vector control; neurocontrollers; predictive control; radial basis function networks; turbogenerators; velocity control; wind turbines; DFM adjustable speed system; doubly fed machine; electromagnetic torque; flux linkage; mechanical motion equation; neural network-based fuzzy predictive current control; radial basis function; voltage dynamic model; Couplings; Current control; Design for manufacture; Electromagnetic modeling; Equations; Fuzzy control; Fuzzy neural networks; Neural networks; Torque; Voltage; doubly fed machines (DFM); dynamic model; intelligent control; wind turbine adjustable speed system;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control, Automation and Systems Engineering, 2009. CASE 2009. IITA International Conference on
Conference_Location :
Zhangjiajie
Print_ISBN :
978-0-7695-3728-3
Type :
conf
DOI :
10.1109/CASE.2009.112
Filename :
5194393
Link To Document :
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