Multiobjective optimization method based on a genetic algorithm for switched reluctance motor design
- 7 August 2002
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Magnetics
- Vol. 38 (3), 1524-1527
- https://doi.org/10.1109/20.999126
Abstract
In this paper, a novel multiobjective optimization method based on a genetic-fuzzy algorithm (GFA) is proposed. The new GFA method is used for optimal design of a switched reluctance motor (SRM) with two objective functions: high efficiency and low torque ripple. The results of the optimal design for an 8/6, four-phase, 4 kW, 250 V, 1500 r.p.m. SRM show improvement in both efficiency and torque ripple of the motor.Keywords
This publication has 11 references indexed in Scilit:
- A variant of evolution strategies for vector optimizationPublished by Springer Nature ,2006
- A niched Pareto genetic algorithm for multiobjective optimizationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- MOGA: multi-objective genetic algorithmsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- An improved magnetic equivalent circuit method for predicting the characteristics of highly saturated electromagnetic devicesIEEE Transactions on Magnetics, 1998
- A multi-objective genetic local search algorithm and its application to flowshop schedulingIEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 1998
- Switched reluctance motor drive systems dynamic performance prediction and experimental verificationIEEE Transactions on Energy Conversion, 1994
- Neural Fuzzy Control Systems with Structure and Parameter LearningPublished by World Scientific Pub Co Pte Ltd ,1994
- ANFIS: adaptive-network-based fuzzy inference systemIEEE Transactions on Systems, Man, and Cybernetics, 1993
- Generating fuzzy rules by learning from examplesIEEE Transactions on Systems, Man, and Cybernetics, 1992
- Nonlinear theory of the switched reluctance motor for rapid computer-aided designIEE Proceedings B Electric Power Applications, 1990