• 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