• DocumentCode
    3049544
  • Title

    Estimation of electrical machine speed using sensorless technology and neural networks

  • Author

    Goedtel, A. ; Silva, N. ; Serni, P. ; Suetake, M.

  • Author_Institution
    Dept. of Electrotech., Univ. of Technol. Parana, Curitiba
  • fYear
    2008
  • fDate
    13-15 Aug. 2008
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    The use of sensorless technologies is an increasing tendency on industrial drivers for electrical machines. The estimation of electrical and mechanical parameters involved with the electrical machine control is used very frequently in order to avoid measurement of all variables related to this process. The cost reduction may also be considered in industrial drivers, besides the increasing robustness of the system, as an advantage of the use of sensorless technologies. This work proposes the use of a recurrent artificial neural network to estimate the speed of induction motor for sensorless control schemes using one single current sensor. Simulation and experimental results are presented to validate the proposed approach.
  • Keywords
    artificial intelligence; electric machine analysis computing; electric machines; machine control; recurrent neural nets; electrical machine control; electrical machine speed estimation; electrical parameter; industrial drivers; mechanical parameter; recurrent artificial neural network; sensorless control schemes; sensorless technology; single current sensor; Artificial neural networks; Costs; Electric variables measurement; Electrical equipment industry; Induction motors; Machine control; Mechanical variables measurement; Neural networks; Robustness; Sensorless control; Induction Motors; Neural Networks; System Identification;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Transmission and Distribution Conference and Exposition: Latin America, 2008 IEEE/PES
  • Conference_Location
    Bogota
  • Print_ISBN
    978-1-4244-2217-3
  • Electronic_ISBN
    978-1-4244-2218-0
  • Type

    conf

  • DOI
    10.1109/TDC-LA.2008.4641832
  • Filename
    4641832