Multiobjective optimization method based on a genetic algorithm for switched reluctance motor design

• Published in: IEEE transactions on magnetics Vol. 38; no. 3; pp. 1524 - 1527
• Main Authors: Mirzaeian, B., Moallem, M., Tahani, V., Lucas, C.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.05.2002; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Applied sciences; Biological cells; Design optimization; Electric motors; Electrical engineering. Electrical power engineering; Electrical machines; Equations; Exact sciences and technology; Genetic algorithms; Genetic mutations; Magnetism; Optimization methods; Reluctance machines; Reluctance motors; Torque; Design; Electrical engineering; Genetic algorithm; Optimization method; Theoretical study; Electric motor; Reluctance motors
• ISSN: 0018-9464; 1941-0069
• DOI: 10.1109/20.999126

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.