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

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2 Author(s)
L. A. Belfore ; Dept. of Electr. & Comput. Eng., Old Dominion Univ., Norfolk, VA, USA ; A. Arkadan

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.

Published in:

IEEE Transactions on Energy Conversion  (Volume:17 ,  Issue: 3 )