Title :
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
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;
Conference_Titel :
Electric Machines and Drives Conference Record, 1997. IEEE International
Conference_Location :
Milwaukee, WI
Print_ISBN :
0-7803-3946-0
DOI :
10.1109/IEMDC.1997.604212
