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Artificial Neural Network Based Rotor Position Estimation for Switched Reluctance Motor

Author

Listed:
  • L.Jessi Sahaya Shanthi
  • R. Arumugam
  • Y.K. Taly
  • S.B. Nandha Kumar

Abstract

Switched Reluctance Motor (SRM) is becoming popular as a variable speed industrial drive. But the requirement of position sensor to synchronize the rotor position with phase currents makes the SRM drive circuit complex and unreliable. With the advent of high speed digital signal processors, it is possible to implement algorithms to estimate the rotor position based on the electrical signals in motor windings. In addition to this, the latest graphical user interface software aids to reduce the time for the development of control algorithms. This paper presents the simulation study of an artificial neural network(ANN) based algorithm for rotor position estimation from  phase voltage and current of a four phase SRM using VISSIM version 6.0B software. Based on the simulation results, a particular artificial neural network (ANN) is selected and checked for real time implementation.

Suggested Citation

  • L.Jessi Sahaya Shanthi & R. Arumugam & Y.K. Taly & S.B. Nandha Kumar, 2008. "Artificial Neural Network Based Rotor Position Estimation for Switched Reluctance Motor," Modern Applied Science, Canadian Center of Science and Education, vol. 2(6), pages 148-148, November.
    • Handle: RePEc:ibn:masjnl:v:2:y:2008:i:6:p:148
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    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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