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
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;
Conference_Titel :
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN :
0-7803-8403-2
DOI :
10.1109/ICMLC.2004.1378560