• DocumentCode
    2850347
  • Title

    Design and Simulation of Artificial-Neural-Network-Based Rotor Resistance Observer of Induction Motors

  • Author

    Sheng-Wei, Gao ; You-Hua, Wang ; Yan, Cai ; Chuang, Zhang

  • Author_Institution
    Province-Minist. Joint Key Lab. of EF & EAR, Hebei Univ. of Technol., Tianjin, China
  • fYear
    2009
  • fDate
    1-3 Nov. 2009
  • Firstpage
    593
  • Lastpage
    596
  • 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. And its variation is very significant because of the temperature rise and skin effect during the implementation of control. A rotor resistance estimation method based on artificial neural network (ANN) is proposed in this paper combining with the voltage model rotor flux and the current model rotor flux. The method can get the suitable rotor resistance value by the provided observer. Simulation results are given to verify that the proposed algorithm is useful to identify the rotor resistance accurately and rapidly so as to enhance its Robustness.
  • Keywords
    electric current control; induction motors; machine vector control; neurocontrollers; observers; rotors; voltage control; artificial-neural-network-based rotor resistance observer; current model rotor flux; induction motors; precise measurements; rotor resistance estimation method; skin effect; temperature rise; vector control; voltage model rotor flux; Angular velocity control; Artificial neural networks; Electric resistance; Induction motors; Intelligent networks; Power system modeling; Rotors; Stators; Synchronous motors; Voltage control; neural networks; parameter identification; rotor resistance; vector control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Networks and Intelligent Systems, 2009. ICINIS '09. Second International Conference on
  • Conference_Location
    Tianjin
  • Print_ISBN
    978-1-4244-5557-7
  • Electronic_ISBN
    978-0-7695-3852-5
  • Type

    conf

  • DOI
    10.1109/ICINIS.2009.156
  • Filename
    5365347