• DocumentCode
    1755194
  • Title

    Accurate Measurement and Detailed Evaluation of Static Electromagnetic Characteristics of Switched Reluctance Machines

  • Author

    Shoujun Song ; Lefei Ge ; Shaojie Ma ; Man Zhang ; Lusheng Wang

  • Author_Institution
    Coll. of Autom., Northwestern Polytech. Univ., Xi´an, China
  • Volume
    64
  • Issue
    3
  • fYear
    2015
  • fDate
    42064
  • Firstpage
    704
  • Lastpage
    714
  • Abstract
    Accurate static electromagnetic characteristics are essential basic data in the modeling of switched reluctance machine (SRM) for performance analysis and advanced control purposes. Because of the doubly salient structure and deep magnetic saturation, the electromagnetic characteristics of SRM have highly nonlinear relations with phase current and rotor position, which make the determination of these characteristics a difficult task. This paper proposes a digital signal processor-based method to accurately measure the flux-linkage and static torque characteristics of SRM. First, theoretical derivation and practical realization of the proposed method are presented in detail, and its accuracy is preliminarily verified by three ways: coenergy method, finite element method, and inductance-capacitance-resistance meter. Then, the errors in measurements are analyzed and corresponding methods to reduce them are given. Finally, with the improved neural network, the nonlinear simulation model of a SRM prototype is built in MATLAB with the measured characteristics, and the simulation results have good agreements with those from experiments, which further verify the accuracy of the proposed method.
  • Keywords
    electromagnetic coupling; finite element analysis; magnetic flux; neural nets; power engineering computing; reluctance machines; rotors; torque; SRM accurate static torque electromagnetic characteristics; advanced control purpose; coenergy method; deep magnetic saturation; digital signal processor; doubly salient structure; finite element method; flux linkage; improved neural network; inductance-capacitance-resistance meter; measurement errors; nonlinear simulation model; performance analysis; phase current; rotor position; switched reluctance machine modeling; Current measurement; Finite element analysis; Reluctance motors; Rotors; Saturation magnetization; Torque; Torque measurement; Error analysis; evaluation; flux linkage; measurement; neural network; switched reluctance machine (SRM); torque;
  • fLanguage
    English
  • Journal_Title
    Instrumentation and Measurement, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9456
  • Type

    jour

  • DOI
    10.1109/TIM.2014.2358132
  • Filename
    6912951