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

IEEE Transactions on Energy Conversion - Tập 17 Số 3 - Trang 380-384 - 2002
L.A. Belfore1, A. Arkadan2
1Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, USA
2Department of Electrical and Computer Engineering, Marquette University, Milwaukee, WI, USA

Tóm tắt

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.

Từ khóa

#Fault tolerance #Reluctance motors #Artificial neural networks #Circuit faults #Coupling circuits #Magnetic circuits #Genetic algorithms #Reluctance machines #DC motors #Synchronous motors

Tài liệu tham khảo

10.1109/60.103639 10.1109/60.300142 10.1109/20.179464 goldberg, 1989, Genetic Algorithms in Search Optimization and Machine Learning 10.1109/20.334074 10.1109/20.43910 10.1109/TPAS.1982.317296 10.1109/60.43257 10.1109/60.84332 10.1109/20.250803 10.1109/20.312632
Thông tin
Thông tin xuất bản

IEEE Transactions on Energy Conversion

Tập 17 Số 3

380-384

Thông tin tác giả