DocumentCode
2295142
Title
Research on online identification of the stator resistance using wavelet neural network
Author
Cao, Cheng-Zhi ; Lu, Mu-Ping ; Zhang, Qi-Dong ; Zhang, Yan-Chao
Author_Institution
Dept. of Inf. & Eng., Shenyang Univ. of Technol., China
Volume
5
fYear
2004
fDate
26-29 Aug. 2004
Firstpage
3073
Abstract
The change of the stator resistance in induction motor greatly affects the performances of direct torque control (DTC) system run at low speeds. It is hard to form an accurate math model, for the change of the resistance value is nonlinear and time varying. According to the terminal temperature of winding and the temperature variation, this paper presents a wavelet neural network used as resistance on-line identification. After trained with recursion arithmetic, the network was used to measure the resistance. The results show that this identifier can precisely measure the value of resistance and efficiently improve the low-speed performances of DTC system.
Keywords
identification; induction motors; learning (artificial intelligence); machine control; neurocontrollers; stators; torque control; wavelet transforms; direct torque control system; induction motor; learning algorithm; mathematical model; recursion arithmetic; resistance online identification; stator resistance; terminal temperature winding; wavelet neural network; Control systems; Discrete wavelet transforms; Electrical resistance measurement; Induction motors; Neural networks; Stators; Temperature; Torque control; Voltage; Wavelet transforms;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN
0-7803-8403-2
Type
conf
DOI
10.1109/ICMLC.2004.1378560
Filename
1378560
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