Advertisement

Journal of Intelligent Manufacturing

, Volume 21, Issue 4, pp 393–402 | Cite as

Intelligent design of induction motors by multiobjective fuzzy genetic algorithm

  • Mehmet Çunkaş
Article

Abstract

In this paper an approach using multi-objective fuzzy genetic algorithm (MFGA) for optimum design of induction motors is presented. Single-objective genetic algorithm optimization is compared with the MFGA optimization. The efficiency of those algorithms is investigated on motor’s performance. The comparison results show that MFGA is able to find more compromise solutions and is promising for providing the optimum design. Besides, a design tool is developed to evaluate and analysis the steady-state characteristics of induction motors.

Keywords

Multiobjective fuzzy optimization Genetic algorithms Induction motor 

Nomenclature

A1m, Ab

Cross-sectional area of stator and rotor conductor, respectively

Ar, Ag

Cross-sectional area of end-ring and air-gap, respectively

Cucost

Cost of unit weight of copper

De

Stator diameter at centers of stator slots

Do

Stator outer diameter

Dr

Rotor diameter

Fecost

Cost of unit weight of iron

few

End winding factor

L1, L2

Axial length of stator and rotor, respectively

m

Number of phase

pfe

Density of the iron sheet

psw, prw

Density of stator and rotor conductors, respectively

Pcu

Total copper losses of stator and rotor

Pfe

Total iron losses

s

Slip

SF

Stacking factor

S1, S2

Number of stator and rotor slot, respectively

wa, wr

Rotor end rings axial and radial width, respectively

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bartz-Beielstein, T., Mehnen, J., Naujoks, B., Schmitt, K., & Zibold, D. (2004). KEA—a software package for development, analysis and application of multiple objective evolutionary algorithms. Technical Report, Reihe comput. Intelli. Collaborative Research Center, University of Dortmound.Google Scholar
  2. Bianchi N., Bolognani S. (1998) Design optimization of electric motors by genetic algorithm. IEE Proceedings Electric Power Applications 145: 475–483CrossRefGoogle Scholar
  3. Bingul, Z., Sekmen, S., Palaniappan, S., & Sabatto, S. (2000). Genetic algorithms applied to real time multiobjective optimization problems. In Proceedings of the IEEE Southeast Congress Conference.Google Scholar
  4. Boldea I., Nasar S.A. (2002) The induction machine handbook. CRC press, Boca RatonGoogle Scholar
  5. Chapman S.J. (2002) Electric machinery and power system fundamentals. McGraw-Hill, New YorkGoogle Scholar
  6. Çunkaş M., Akkaya R. (2006) Design optimization of induction motor by genetic algorithm and comparison with existing motor. Mathematical and Computational Applications 11(3): 193–203Google Scholar
  7. Çunkaş M., Akkaya R., Bilgin O. (2007) Cost optimization of submersible motors using a genetic algorithm and a finite element method. International Journal of Advanced Manufacturing Technology 33: 223–232. doi: 10.1007/s00170-006-0458-x CrossRefGoogle Scholar
  8. Faiz J., Sharifian M.B.B. (2001) Optimal design of three phase induction motors and their comparison with a typical industrial motor. International Journal of Computers and Electrical Engineering 27: 133–144. doi: 10.1016/S0045-7906(00)00010-0 CrossRefGoogle Scholar
  9. Faiz J., Sharifian M.B.B., Keyhani A., Proca A. (2000) Performance comparison of optimally designed induction motors with aluminum and copper squirrel-cages. Electric Machinery and Power Systems 28: 1195–1207. doi: 10.1080/073135600449062 CrossRefGoogle Scholar
  10. Fci R., Fuchs E.F., Huaugh H. (1989) Comparison of two optimization techniques as applied to three-phase induction motor design. IEEE Transactions on Energy Conversion 4(4): 651–659. doi: 10.1109/60.41724 CrossRefGoogle Scholar
  11. Göl Ö., Sobhi-Najafabadi B. (2005) Use of evolutionary techniques for multi-objective optimisation of electromotion devices. Journal of Materials Processing Technology 161: 300–304. doi: 10.1016/j.jmatprotec.2004.07.040 CrossRefGoogle Scholar
  12. Hamarat S., Leblebicioğlu K., Ertan H.B. (1998). Comparison of deterministic and non-deterministic optimization algorithms for design optimization of electrical machines. ICEM, Istanbul, Turkey, pp. 1477–1482.Google Scholar
  13. Holland, J. H. (1970). Robust algorithms for adaptation set in a general formal framework, In Proceedings of the IEEE Symposium on Adaptive Processes in Decision and Control (vol. XVII, Sect. 5.1). NY: ACM Press.Google Scholar
  14. Hsu, L. Y., Tsai, M. C., & Huang, C. C. (2003). Efficiency optimization of brushless permanent magnet motors using penalty genetic algorithms. IEEE Electric Machines and Drives Conference, IEMDC’03 (vol. 1, pp. 365–369).Google Scholar
  15. Huang H.Z., Gu Y.K., Du X. (2006) An interactive fuzzy multi-objective optimization method for engineering design. Engineering Applications of Artificial Intelligence 19: 451–460. doi: 10.1016/j.engappai.2005.12.001 CrossRefGoogle Scholar
  16. Kim M.K., Lee C.G., Jung H.K. (1998) Multiobjective optimal design of three-phase induction motor using improved evolution strategy. IEEE Transactions on Magnetics 34: 2980–2983. doi: 10.1109/20.717696 CrossRefGoogle Scholar
  17. Liuzzi G., Lucidi S., Parasiliti F., Villani M. (2003) Multiobjective optimization techniques for the design of induction motors. IEEE Transactions on Magnetics 39: 1261–1264. doi: 10.1109/TMAG.2003.810193 CrossRefGoogle Scholar
  18. Minghua Y., Changwen X. (1994) Multiobjective fuzzy optimization of structures based on generalized fuzzy decision-making. Computers and Structures 53(2): 411–417. doi: 10.1016/0045-7949(94)90213-5 CrossRefGoogle Scholar
  19. Mirzaeian B., Moallem M., Tahani V., Lucas C. (2002) Multiobjective optimization method based on a genetic algorithm for switched reluctance motor design. IEEE Transactions on Magnetics 38: 1524–1527. doi: 10.1109/20.999126 CrossRefGoogle Scholar
  20. Pillay P., Nolan R., Hague T. (1997) Application of Genetic algorithms to motor parameter determination for transient torque calculations. IEEE Transactions on Industry Applications 33(5): 1273–1282. doi: 10.1109/28.633806 CrossRefGoogle Scholar
  21. Sbalzarini, I. B., Müller, S., & Koutsakos, P. (2000). Multiobjective optimization using evolutionary algorithms, Center for Turbulence Research, Proceedings of Summer Programs (pp. 63–74).Google Scholar
  22. Shih C.J., Lai T.K. (1994) Fuzzy weighting optimization with several objective functions in structural design. Computers and Strutures 52(5): 917–924CrossRefGoogle Scholar
  23. Sudhoff, S. D., Cale, J., Cassimere, B., & Swinney, M. (2005). Genetic algorithm based design of a permanent magnet synchronous machine. Electric Machines and Drives, IEEE International Conference (pp. 1011–1019).Google Scholar
  24. Tan K.C., Lee T.H., Khoo D., Khoor E.F. (2001) A multiobjective evolutionary algorithm toolbox for computer-aided multiobjective optimization. IEEE Transactions on Systems, Man, and Cybernetics-Part B. Cybernetics 31(4): 537–556CrossRefGoogle Scholar
  25. Trebi-Ollennu A., White B.A. (1997) Multiobjective fuzzy genetic algorithm optimization approach to nonlinear control system design. IEE Proceedings of Control Theory and Applications 144: 137–142. doi: 10.1049/ip-cta:19971031 CrossRefGoogle Scholar
  26. Veinott C.G. (1959) Theory and design of small induction motors. McGraw-Hill, New YorkGoogle Scholar
  27. Wieczorek J.P., Göl Ö., Michalewicz Z. (1998) An evolutionary algorithm for the optimal design of induction motors. IEEE Transactions on Magnetics 34(6): 3882–3887. doi: 10.1109/20.728298 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Department of Electronics and Computer EducationSelçuk UniversityKonyaTurkey

Personalised recommendations