Abstract
This study presents design optimization of permanent magnet synchronous motor by using different artificial intelligence methods. For this purpose, three stochastic optimization methods—genetic algorithm, simulated annealing, and differential evolution—were used. Motor design parameters and efficiency results obtained by the artificial intelligence methods were compared with each other. The results were later checked by finite element analysis. Consequently, the motor efficiencies obtained from the algorithms have high accuracy. Approaches strategies of the artificial intelligence algorithms are quite sufficient and remarkable for design optimization of permanent magnet synchronous motor. The differential evolution is better and more reliable optimization method nevertheless.
Similar content being viewed by others
References
Shen Q, Sun N, Zhao G, Han X, Tang R (2010) Design of a permanent magnet synchronous motor and performance analysis for subway. In: XIX International Conference on Electrical Machines (ICEM). pp 1–6
Bianchi N, Bolognani S (2002) Design methods for reducing the cogging torque in surface-mounted PM motors. IEEE Trans Indus Appl 38(5):1259–1265
Chin YK, Soulard J (2003) A permanent magnet synchronous motor for traction applications of electric vehicles. IEEE Inter Elect Mach Drives Conf (IEMDC) 2:1035–1041
El-Refaie AM (2010) Fractional-slot concentrated-windings synchronous permanent magnet machines—opportunities and challenges. IEEE Trans Indus Elect 57(1):107–121
Salminen P, Pyrhönen J, Libert F, Soulard J (2005) Torque ripple of permanent magnet machines with concentrated windings. In: XII International Symposium on Electrical Fie in mechat, electrical and electronical engineering, (ISEF)
Zhu ZQ, Howe D (2000) Influence of design parameters on cogging torque in permanent magnet machines. IEEE Trans Energy Convers 15(4):407–412
Bianchi N, Bolognani S (1998) Design optimisation of electric motors by genetic algorithms. IEE Proc Electr Power Appl 145:475–483
Hwang CC, Lyu LY, Liu CT, Li PL (2008) Optimal design of an SPM motor using genetic algorithms and taguchi method. IEEE Trans Magn 44(11):4325–4328
Cassimere BN, Sudhoff S (2009) Population-based design of surface-mounted permanent-magnet synchronous machines. IEEE Trans on Energy Con 24(2):338–346
Michalewicz Z (1996) Genetic algorithms + data structures = evolution programs—third, revised and extended edition. Springer, Berlin
Yang XS (2010) Engineering optimization—an introduction with metaheuristic applications. Wiley, Hoboken
Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Sci New Ser 220(4598):671–680
Rao SS (2009) Engineering optimization theory and practice, 4th edn. Wiley, Hoboken
Storn R, Price K (1995). Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical report TR-95-012, International Computer Science Institute, Berkley
Price KV, Storn RM, Lampinen JA (2005) Differential evolution—a practical approach to global optimization. Heidelberg, Berlin
Libert F, Soulard J (2004) Design study of different direct-driven permanent-magnet motors for a low speed application. In: Proceedings of the Nordic Workshop on Power and Indus Electro (NORPIE)
Pyrhonen J, Jokinen T, Hrabovcova V (2008) Design of rotating electrical machines. Wiley, New York
Flux2D, Version 10.3, Tutorial: Brushless Embedded Magnet motor, CEDRAT (2010)
Libert F (2004) Design, optimization and comparison of permanent magnet motors for a low-speed direct-driven mixer. Technical Licentiate, School of Computer Science, Electrical Engineering and Engineering Physics. KTH, Sweden
Acknowledgments
This study is supported by the Scientific Research Projects of Selçuk University.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Mutluer, M., Bilgin, O. Comparison of stochastic optimization methods for design optimization of permanent magnet synchronous motor. Neural Comput & Applic 21, 2049–2056 (2012). https://doi.org/10.1007/s00521-011-0627-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-011-0627-1