DocumentCode :
2279275
Title :
Design and Simulation of Rotor Resistance Observer for Induction Motors Using Artificial Neural Network
Author :
Gao Sheng-Wei ; Wang You-hua ; Cai Yan ; Zhang Chuang
Author_Institution :
Province-Minist. Joint Key Lab. of EF & EAR, Hebei Univ. of Technol., Tianjin, China
Volume :
1
fYear :
2010
fDate :
13-14 March 2010
Firstpage :
974
Lastpage :
977
Abstract :
The performance of the vector control depends on the precise measurements of parameters in motor. The rotor resistance is one of the most important parameters. An adaptive scheme for on-line identification of the rotor resistance based on the artificial neural networks is proposed in this paper. By using the BP algorithm theory, the rotor flux error between the voltage model and the neural network model is back propagated to adjust the weights of the neural network model which can be used to calculate the rotor resistance. The results of simulation are given to verify that the neural network observer can identify the rotor resistance accurately and rapidly. At the same time it has good robustness performance.
Keywords :
backpropagation; induction motors; magnetic flux; neural nets; nonlinear control systems; observers; parameter estimation; rotors; stability; BP algorithm theory; artificial neural network; induction motor; parameter identification; rotor flux error; rotor resistance observer; vector control; voltage model; Angular velocity control; Artificial neural networks; Electric resistance; Electrical resistance measurement; Induction motors; Power system modeling; Rotors; Stators; Synchronous motors; Voltage control; Induction Motor; Neural Networks; Parameter Identification; Rotor Resistance; Vector Control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Measuring Technology and Mechatronics Automation (ICMTMA), 2010 International Conference on
Conference_Location :
Changsha City
Print_ISBN :
978-1-4244-5001-5
Electronic_ISBN :
978-1-4244-5739-7
Type :
conf
DOI :
10.1109/ICMTMA.2010.163
Filename :
5458680
Link To Document :
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