DocumentCode :
3132646
Title :
The research of double-fed motor based on the neural network inverse system control strategy and it´s simulation
Author :
Liao, Dongchu ; Bie, Wei ; Ou, Wenjun ; Xiong, Dawei ; Yang, Zhilin
Author_Institution :
Dept. of Electr. & Electron. Eng., Hubei Univ. of Technol., Wuhan, China
Volume :
2
fYear :
2011
fDate :
20-21 Aug. 2011
Firstpage :
279
Lastpage :
283
Abstract :
In this paper, based on vector control system of double-fed motor, the artificial neural network (ANN) inverse control strategy of double-fed motor control is discussed. The double-fed motor mathematical model with stator flux oriented in the synchronous MT reference frame is given, and the reversibility of the system through Interact or Algorithm is confirmed, thus the artificial neural network inverse system model of motor is contributed, then the model is applied to double-fed speed-regulating system. At last, the double-fed motor control strategy based on artificial neural network inverse model is simulated on MATLAB. The simulation results shows that applying the neural network inverse system control strategy to double-fed motor is feasible.
Keywords :
inverse problems; machine vector control; neurocontrollers; stators; synchronous motors; ANN inverse control strategy; MATLAB; artificial neural network inverse control strategy; artificial neural network inverse model; artificial neural network inverse system model; double-fed motor control strategy; double-fed motor mathematical model; double-fed speed-regulating system; neural network inverse system control strategy; stator flux; synchronous MT reference frame; system reversibility; vector control system; Artificial neural networks; Biological neural networks; Induction motors; Mathematical model; Rotors; Stator windings; artificial neural network(ANN); decoupling control; double-fed motor; inverse system;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computing, Control and Industrial Engineering (CCIE), 2011 IEEE 2nd International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-9599-3
Type :
conf
DOI :
10.1109/CCIENG.2011.6008119
Filename :
6008119
Link To Document :
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