• DocumentCode
    1747119
  • Title

    Study on optimal driving condition of SRM using GA-neural network

  • Author

    Oh, Seok-Gyu ; Ahn, Jin-Woo ; Lee, Young-Jin ; Lee, Man-Hyung

  • Author_Institution
    Dept. of IA, Chinju Nat. Univ., South Korea
  • Volume
    2
  • fYear
    2001
  • fDate
    2001
  • Firstpage
    1382
  • Abstract
    The torque of SRM depends on phase current and the derivative of inductance. But the inductance of SRM is nonlinearly changed according to rotor position angle and phase current because of saturation in magnetic circuit. Therefore this has a concern in torque ripple and speed variation, and it is difficult to control the desired torque. This paper proposes an optimization control scheme by adjusting both the turn-on and turn-off angle according to high efficiency points which are simulated by GA-neural network, which is used to simulate the reasonable switching angle which is nonlinearly varied with rotor speed and load
  • Keywords
    angular velocity control; genetic algorithms; inductance; machine control; magnetic circuits; neural nets; reluctance motor drives; rotors; torque control; GA-neural network; SRM; high efficiency points; load; magnetic circuit saturation; optimal driving condition; optimization control; phase current; reasonable switching angle simulation; rotor position angle; rotor speed; speed control; speed variation; switched reluctance motor drive; torque; torque ripple; turn-off angle; turn-on angle; Circuit simulation; Inductance; Magnetic circuits; Reluctance machines; Reluctance motors; Rotors; Shape; Switches; Torque control; Voltage control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial Electronics, 2001. Proceedings. ISIE 2001. IEEE International Symposium on
  • Conference_Location
    Pusan
  • Print_ISBN
    0-7803-7090-2
  • Type

    conf

  • DOI
    10.1109/ISIE.2001.931684
  • Filename
    931684