• DocumentCode
    2287654
  • Title

    Induction Machine Fault Identification using Particle Swarm Algorithms

  • Author

    Etny, S.A. ; Acarlney, P.P. ; Zahawi, B. ; Giaouris, D.

  • Author_Institution
    Sch. of Electr. & Comput. Eng., Newcastle Univ.
  • fYear
    2006
  • fDate
    12-15 Dec. 2006
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    The principles of a new technique using particle swarm algorithms for condition monitoring of the stator and rotor circuits of an induction machine is described in this paper. Using terminal voltage and current data, the stochastic optimization technique is able to indicate the presence of a fault and provide information about the location and nature of the fault. The technique is demonstrated using experimental data from a laboratory machine with both stator and rotor winding faults.
  • Keywords
    asynchronous machines; condition monitoring; fault location; particle swarm optimisation; stochastic programming; condition monitoring; fault identification; fault location; induction machine; particle swarm algorithm; stochastic optimization; Circuit faults; Condition monitoring; Fault diagnosis; Induction machines; Laboratories; Machine windings; Particle swarm optimization; Stator windings; Stochastic processes; Voltage; Condition monitoring; induction machine; stochastic optimization; swarm algorithms;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power Electronics, Drives and Energy Systems, 2006. PEDES '06. International Conference on
  • Conference_Location
    New Delhi
  • Print_ISBN
    0-7803-9772-X
  • Electronic_ISBN
    0-7803-9772-X
  • Type

    conf

  • DOI
    10.1109/PEDES.2006.344310
  • Filename
    4148017