Title :
Performance prediction of SRM drive systems under normal and fault operating conditions using GA-based ANN method
Author :
Arkadan, A.A. ; Du, P. ; Sidani, M. ; Bouji, M.
Author_Institution :
Marquette Univ., Milwaukee, WI, USA
fDate :
7/1/2000 12:00:00 AM
Abstract :
A method to predict the performance characteristics of switched reluctance motor (SRM) drive systems under normal and fault operating conditions is presented. The method uses a genetic algorithm (GA) based artificial neural networks (ANNs) approach which is applied for its interpolation capabilities for highly nonlinear systems in order to obtain a fast and accurate prediction of the performance of the SRM drive system
Keywords :
electric machine analysis computing; genetic algorithms; interpolation; machine theory; neural nets; nonlinear systems; reluctance motor drives; SRM drive systems; artificial neural networks; computer simulation; fault operating conditions; genetic algorithm; highly nonlinear systems; interpolation capabilities; normal operating conditions; performance characteristics; switched reluctance motor; Artificial neural networks; Circuit faults; Inductance; Interpolation; Iron; Magnetic fields; Magnetostatics; Reluctance machines; Reluctance motors; State-space methods;
Journal_Title :
Magnetics, IEEE Transactions on
