• DocumentCode
    1185045
  • Title

    A Methodology for Characterizing Fault Tolerant Switched Reluctance Motors Using Neurogenetically Derived Models

  • Author

    Belfore, L. ; Arkadan, A. A.

  • Author_Institution
    Old Dominoin University, Norfolk, VA; Marquette University, Milwaukee, WI
  • Volume
    22
  • Issue
    7
  • fYear
    2002
  • fDate
    7/1/2002 12:00:00 AM
  • Firstpage
    48
  • Lastpage
    48
  • Abstract
    This paper examines the feasibility of using artificial neural networks (ANNs) and genetic algorithms (GAs) to develop discrete time dynamic models for fault free and faulted switched reluctance motor (SRM) drive systems. The results of using the ANN-GAbased (nenrogenetic) model to predict the performance characteristics ofprototype SRM drive motor under normal and abnormal operating conditions are presented and verified by comparison to teat data.
  • Keywords
    Artificial neural networks; Drives; Fault tolerance; Finite element methods; Genetic algorithms; Neural networks; Predictive models; Reluctance machines; Reluctance motors; Synchronous motors; Synchronous motors; fault tolerance; finite element methods; genetic algorithms; neural networks;
  • fLanguage
    English
  • Journal_Title
    Power Engineering Review, IEEE
  • Publisher
    ieee
  • ISSN
    0272-1724
  • Type

    jour

  • DOI
    10.1109/MPER.2002.4312350
  • Filename
    4312350
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1185045