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Effect of Geometrical Parameters on Optimal Design of Synchronous Reluctance Motor

  • Nagarajan, V.S. (Electrical and Electronics Engineering, SSN College of Engineering) ;
  • Kamaraj, V. (Electrical and Electronics Engineering, SSN College of Engineering) ;
  • Balaji, M. (Electrical and Electronics Engineering, SSN College of Engineering) ;
  • Arumugam, R. (Electrical and Electronics Engineering, SSN College of Engineering) ;
  • Ganesh, N. (Renault-Nissan Technology and Business Centre India Private Ltd) ;
  • Rahul, R. (Electrical and Electronics Engineering, SSN College of Engineering) ;
  • Lohit, M. (Mechanical Engineering, SSN College of Engineering)
  • Received : 2016.07.29
  • Accepted : 2016.10.18
  • Published : 2016.12.31

Abstract

Torque ripple minimization without decrease in average torque is a vital attribute in the design of Synchronous Reluctance (SynRel) motor. As the design of SynRel motor is an arduous task, which encompasses many design variables, this work first analyses the significance of the effect of varying the geometrical parameters on average torque and torque ripple and then proposes an extensive optimization procedure to obtain configurations with improved average torque and minimized torque ripple. A hardware prototype is fabricated and tested. The Finite Element Analysis (FEA) software tool used for validating the test results is MagNet 7.6.0.8. Multi Objective Particle Swarm Optimization (MOPSO) is used to determine the various designs meeting the requirements of reduced torque ripple and improved torque performance. The results indicate the efficacy of the proposed methodology and substantiate the utilization of MOPSO as a significant tool for solving design problems related to SynRel motor.

Acknowledgement

Supported by : Department of Science and Technology

References

  1. Boldea, Reluctance Synchronous Machines and Drives, Clarendon press (1996).
  2. T. Lipo, Electric Machines & Power Systems 19, 659 (1991). https://doi.org/10.1080/07313569108909556
  3. N. Bianchi, M. Degano, and E. Fornasiero, IEEE Trans. Ind. Appl. 51, 187 (2015). https://doi.org/10.1109/TIA.2014.2327143
  4. M. H. Yoon, D. Y. Kim, S. I. Kim, and J. P. Hong, J. Magn. 20, 387 (2015). https://doi.org/10.4283/JMAG.2015.20.4.387
  5. R. R. Moghaddham, Master's Thesis, KTH University, Sweden (2007).
  6. R. R. Moghaddam and F. Gyllensten, IEEE Trans. Ind. Appl. 61, 5058 (2014).
  7. G. Pellegrino, F. Cupertino, and C. Gerada, IEEE Trans. Ind. Appl. 51, 1465 (2015). https://doi.org/10.1109/TIA.2014.2345953
  8. F. Cupertino, G. Pellegrino, and C. Gerada, IEEE Trans. Ind. App. 50, 3617 (2014). https://doi.org/10.1109/TIA.2014.2312540
  9. M. Gamba, G. Pelegrino, and F. Cupertino, ICEM, Berlin 1334 (2014).
  10. K. S. Khan, Master Thesis, KTH University, Sweden (2011).
  11. K. Wang, Z. Q. Zhu, G. Ombach, M. Koch, S. Zhang, and J. Xu COMPEL 34, 18 (2015). https://doi.org/10.1108/COMPEL-11-2013-0367
  12. E. Howard, M. J. Kamper, and S. Gerber, IEEE Trans. Ind. Appl. 51, 3751 (2015). https://doi.org/10.1109/TIA.2015.2429649
  13. S. Taghavi and P. Pillay, ECCE, 5131 (2014).
  14. K. Deb, Multi-Objective Optimization using Evolutionary Algorithms Wiley, Singapore (2001).
  15. P. Di Barba, Multiobjective Shape Design in Electricity and Magnetism, Springer (2010).
  16. S. H. Kam and T. U. Jung, J. Magn. 20, 91 (2015). https://doi.org/10.4283/JMAG.2015.20.1.091
  17. M. Yaxdani-Asrami, M. Alipour, and S. Asghar Gholamian, J. Magn. 20, 161 (2015). https://doi.org/10.4283/JMAG.2015.20.2.161
  18. J. Baek, S. Kwak, and H. A. Toliyat, J. Magn. 18, 65 (2013). https://doi.org/10.4283/JMAG.2013.18.1.065
  19. R. Eberhart and J. Kennedy, MHS 43 (1995).
  20. J. Kennedy and R. Eberhart, ICNN 4, 1948 (1995).
  21. C. A. C. Coello, G. T. Pulido, and M. S. Lechuga, IEEE Trans. Evol. Comput. 8, 279 (2004).
  22. N. Umadevi, M. Balaji V. Kamaraj, and L. Ananda Padmanaban, COMPEL 34, 1318 (2015).
  23. H. Sahraoui, H. Zeroug, and H. A. Toliyat, IEEE Trans. Magn. 43, 4095 (2007).
  24. W. Yan, IEEE Trans. Neural Netw. Learn. Syst. 23, 1039 (2012).
  25. M. Balaji and V. Kamaraj, JEPE, 63 (2012).
  26. K. Wang, Z. Q. Zhu, G. Ombach, M. Koch, S. Zhang, and J. Xu, J. Magn. 34, 3 (2015).
  27. S. A. Hong, J. Y. Choi, and S. M. Jang, J. Magn. 19, 84 (2014). https://doi.org/10.4283/JMAG.2014.19.1.084
  28. S. H. Lee, Y. J. Kim, K. S. Lee, and S. J. Kim, J. Magn. 20, 444 (2015). https://doi.org/10.4283/JMAG.2015.20.4.444
  29. J. H. Lee and I. K. Lee, J. Magn. 15, 85 (2010). https://doi.org/10.4283/JMAG.2010.15.2.085
  30. J. H. Lee and A. R. Jeon, J. Magn. 15, 138 (2010). https://doi.org/10.4283/JMAG.2010.15.3.138
  31. http://www.infolytica.com
  32. http://www.mathworks.com