• DocumentCode
    2938604
  • Title

    Research of Switched Reluctance Generator Position Sensorless Based on ANFIS by Using Finite Element Analysis

  • Author

    Yi Ling-Zhi ; Peng Han mei ; Liu Xiang ; Wang Gen ping

  • Author_Institution
    Coll. of Inf. Eng., Xiangtan Univ., Xiangtan, China
  • fYear
    2011
  • fDate
    25-28 March 2011
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    A new method of SRG Position Sensorless based on Self-adaptive Fuzzy Neural Network by using Finite Element Analysis is proposed in this paper. Through current and magnetic linkage to get angle of SRG rotor position, so the nonlinear mapping of current-magnetic linkage-angle can be built. By training these sample data, the angle of SRG rotor position can replaced by the output of ANFIS, to achieve SRG Position Sensorless. Simulation results show that the error of between actual rotor position and estimate rotor position is small, SRG can commutate with great accuracy, the output voltage of SRG in variable-speed directly driven wind power generation systems is essentially constant, and SRG is working well.
  • Keywords
    finite element analysis; fuzzy neural nets; learning (artificial intelligence); power engineering computing; reluctance generators; wind power plants; ANFIS; current linkage; current-magnetic linkage-angle; finite element analysis; magnetic linkage; nonlinear mapping; rotor position; self-adaptive fuzzy neural network; switched reluctance generator position sensorless; wind power generation system; Artificial neural networks; Couplings; Finite element methods; Magnetic domains; Rotors; Training; Wind speed;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power and Energy Engineering Conference (APPEEC), 2011 Asia-Pacific
  • Conference_Location
    Wuhan
  • ISSN
    2157-4839
  • Print_ISBN
    978-1-4244-6253-7
  • Type

    conf

  • DOI
    10.1109/APPEEC.2011.5748973
  • Filename
    5748973