A fuzzy-genetic based design of permanent magnet synchronous motor
AbstractThis paper presents a fuzzy-genetic based design of permanent magnet synchronous motor. The selected motor structure with surface magnet and double layer winding is for high torque and low speed applications. The design approach involves combining fuzzy logic and genetic algorithm in a powerful combination. While the genetic algorithm is used in scanning of the solution space, the fuzzy logic approach has been utilized in selecting the most appropriate solutions. While choosing geometric parameters as input for optimization, design equations are obtained by using geometrical, electrical and magnetic properties of the motor. The output results are evaluated with motor efficiency, motor weight and weight of magnets as the objective function. Furthermore, the multiobjective design optimization results are compared with the results obtained for each single objective and tested with finite element method. The results are finally remarkable and quite compatible with the finite element method results.
J. R. Hendershot, T.H.E. Miller (1994). Design of brushless permanent magnet motors. Oxford University Press Inc., New York.
D. C. Hanselman (2006). Brushless permanent magnet motor design. Magna Physics Publishing, Ohio.
S. D. Sudhoff (2014). Power magnetic devices: a multi-objective design approach. John Wiley&Sons Inc., New Jersey.
B. N. Cassimere, S. Sudhoff (2009). Population-based design of surface-mounted permanent-magnet synchronous machines. IEEE Transactions on Energy Conversion, Vol. 24, No. 2, pp 338-346, doi:10.1109/TEC.2009.2016150.
Y. Duan, R. G. Harley, Y. G. Habetler (2009). Comparison of particle swarm optimization and genetic algorithm in the design of permanent magnet motors. IEEE 6th International Power Electronics and Motion Control Conference, pp 822-825, doi:10.1109/IPEMC.2009.5157497.
G. Zhang, M. Dou, S. Wang (2009). Hybrid genetic algorithm with particle swarm optimization technique. International Conference on Computational Intelligence and Security, Vol. 1, pp 103-106, doi:10.1109/CIS.2009.236.
W. H. Ho, J. T. Tsai, J. H. Chou, J. B. Yue (2016). Intelligent hybrid taguchi-genetic algorithm for multi-criteria optimization of shaft alignment in marine vessel. IEEE Access, Vol. 4, pp 2304-2313, doi:10.1109/ACCESS.2016.2569537.
K. Pytel (2016). Hybrid fuzzy-genetic algorithm applied to clustering problem. Proceedings of the Federated Conference on Computer Science and Information Systems, pp. 137-140, INSPEC Accession Number:16428579.
A. Wang, Y. Wen, W. L. Soong, H. Li (2016). Application of a hybrid genetic algorithm for optimal design of interior permanent magnet synchronous machines. IEEE Conference on Electromagnetic Field Computation, pp 1-1, doi:10.1109/CEFC.2016.7816299.
J. T. Park, C. G. Lee, M. K. Kim, H. K. Jung (1997). Application of fuzzy decision to optimization of induction motor design. IEEE Transactions On Magnetics, Vol. 33, No. 2, pp 1939-1942, doi:10.1109/20.582672.
E. Koskimäki, J. Göös (1997). Electric machine dimensioning by global optimization. First International Conference on Knowledge-Based Intelligent Electronic Systems, doi:10.1109/KES.1997.616930.
B. Mirzaeian, M. Moallem, V. Tahani, C. Lucas (2002). Multiobjective optimization method based on a genetic algorithm for switched reluctance motor design. IEEE Transactions On Magnetics, Vol. 38, No. 3, pp 1524-1527, doi:10.1109/20.999126.
S. Owatchaiphong, N. H. Fuengwarodsakul (2009). Multi-objective based optimization for switched reluctance machines using fuzzy and genetic algorithms. International Conference on Power Electronics and Drive Systems, doi:10.1109/PEDS.2009.5385926.
C. Choi, D. Lee, K. Park (2000). Fuzzy design of a switched reluctance motor based on the torque profile optimization. IEEE Transactions On Magnetics, Vol. 36, No. 5, pp 3548-3550, doi:10.1109/20.908894.
J. Pyrhonen, T. Jokinen, V. Hrabovcová (2008). Design of rotating electrical machines. John Wiley & Sons Ltd.
F. Libert (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.
F. Libert, J. Soulard (2004). Design study of different direct-driven permanent-magnet motors for a low speed application. Proc of the Nordic Workshop on Power and Indus Electro (NORPIE).
M. Mutluer, O. Bilgin (2016). An intelligent design optimization of a permanent magnet synchronous motor by artificial bee colony algorithm. Turkish Journal of Electrical Engineering & Computer Sciences, Vol. 24, pp 1826-1837, doi:10.3906/elk-1311-150.
M. Mutluer, O. Bilgin (2012) Comparison of stochastic optimization methods for design optimization of permanent magnet synchronous motor. Neural Computing and Applications, Vol. 21, No 8, pp 2049-2056, doi:10.1007/s00521-011-0627-1.
M. Çunkaş (2008). Intelligent design of induction motors by multiobjective fuzzy-genetic algorithm, Journal of Intelligent Manufacturing, 21 (4) 393-402.
A. Trebi-Ollennu, B. A. White (1997). Multiobjective fuzzy-genetic algorithm optimization approach to nonlinear control system design. IEE Proceedings of Control Theory and Applications, 144 2.
Z. Michalewicz (1996). Genetic algorithms + data structures = evolution programs - third, revised and extended edition. Springer-Verlag, Berlin, Heidelberg.
X. S. Yang (2010). Engineering optimization – an introduction with metaheuristic applications. John Wiley & Sons, Inc., Hoboken, New Jersey.
S. S. Rao (2009). Engineering optimization theory and practice – fourth edition. John Wiley & Sons, Inc., Hoboken, New Jersey.
Flux2D, Version 10.3, Tutorial: Brushless Embedded Magnet motor, CEDRAT, 2010.
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