• 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