Neurogenetic models for the characterization of fault tolerant switched reluctance motors

Author

Belfore, Lee A., II ; Arkadan, Abdul-Rahman A.

Author_Institution

Dept. of Electr. & Comput. Eng., Marquette Univ., Milwaukee, WI, USA

fYear

1997

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 prototype SRM drive motor under normal and abnormal operating conditions, are presented and verified by comparison to test data

Keywords

discrete time systems; electric machine analysis computing; genetic algorithms; machine theory; neural nets; reliability; reluctance motor drives; abnormal operating conditions; artificial neural networks; discrete time dynamic models; fault free switched reluctance motors; fault tolerant switched reluctance motors; faulted switched reluctance motors; genetic algorithms; neurogenetic models; normal operating conditions; performance characteristics prediction; switched reluctance motor drives; Artificial neural networks; Circuit faults; DC motors; Delay; Fault tolerance; Genetic algorithms; Predictive models; Reluctance machines; Reluctance motors; State-space methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Electric Machines and Drives Conference Record, 1997. IEEE International

Conference_Location

Milwaukee, WI

Print_ISBN

0-7803-3946-0

Type

conf

DOI

10.1109/IEMDC.1997.604212

Filename

604212