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
fDate :
7/1/2002 12:00:00 AM
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;
Journal_Title :
Power Engineering Review, IEEE
DOI :
10.1109/MPER.2002.4312350
