A methodology for characterizing fault tolerant switched reluctance motors using neurogenetically derived models

Author

Belfore, Lee A., II ; Arkadan, A.

Author_Institution

Dept. of Electr. & Comput. Eng., Old Dominion Univ., Norfolk, VA, USA

Volume

17

Issue

3

fYear

2002

fDate

9/1/2002 12:00:00 AM

Firstpage

380

Lastpage

384

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-GA-based (neurogenetic) model to predict the performance characteristics of a prototype SRM drive motor under normal and abnormal operating conditions are presented and verified by comparison to test data.

Keywords

electric machine analysis computing; electrical faults; fault diagnosis; fault tolerance; finite element analysis; genetic algorithms; neural nets; reluctance motor drives; artificial neural networks; discrete time dynamic models; fault tolerant SRM characterisation; fault-free SRM drives; faulted SRM drives; genetic algorithms; neurogenetically derived models; switched reluctance motors; Artificial neural networks; Circuit faults; Coupling circuits; DC motors; Fault tolerance; Genetic algorithms; Magnetic circuits; Reluctance machines; Reluctance motors; Synchronous motors;

fLanguage

English

Journal_Title

Energy Conversion, IEEE Transactions on

Publisher

ieee

ISSN

0885-8969

Type

jour

DOI

10.1109/TEC.2002.801999

Filename

1033965