Advertisement

A New Surrogate-assisted Robust Multi-objective Optimization Algorithm for an Electrical Machine Design

  • Dong-Kuk Lim
  • Dong-Kyun WooEmail author
Original Article

Abstract

For a multi-objective optimization problem applied to the electric machine design, a new surrogate-assisted robust algorithm is proposed in this research. The proposed algorithm can find a robust and well-distributed Pareto front set rapidly and precisely for robust nondominated solutions using a surrogate model and an uncertainty consideration with a worst-case scenario. The outstanding performances of the proposed algorithm are verified by test functions. Furthermore, through the application of the optimal design process of a surface-mounted permanent magnet synchronous motor for an electric bicycle, the feasibility of this algorithm is verified.

Keywords

Multi-objective Nondominated solution Surface-mounted permanent magnet synchronous motor Surrogate model 

Notes

Acknowledgements

This work was supported by the 2017 Research Fund of University of Ulsan.

References

  1. 1.
    Steiner G, Weber A, Magele C (2004) Managing uncertainties in electromagnetic design problems with robust optimization. IEEE Trans Magn 40(2):1094–1099CrossRefGoogle Scholar
  2. 2.
    Mendes MHS, Soares GL, Coulomb JL, Vasconcelos JA (2013) A surrogate genetic programming based model to facilitate robust multi-objective optimization: a case study in magnetostatics. IEEE Trans Magn 49(5):2065–2068CrossRefGoogle Scholar
  3. 3.
    Xia B, Ren Z, Koh CS (2014) Utilizing kriging surrogate models for multi-objective robust optimization of electromagnetic devices. IEEE Trans Magn 50(2):693–696 (Art. ID 7017104) CrossRefGoogle Scholar
  4. 4.
    Xiao S, Liu GQ, Zhang KL, Jing YZ, Duan JH, Barba PD, Sykulski JK (2018) Multi-objective pareto optimization of electromagnetic devices exploiting kriging with lipschitzian optimized expected improvement. IEEE Trans. Magn 54(3):1–4 (Art. ID 7001704) CrossRefGoogle Scholar
  5. 5.
    Kim IW, Woo DK, Lim DK, Jung SY, Lee CG, Ro JS, Jung HK (2014) Minimization of a cogging torque for an interior permanent magnet synchronous machine using a novel hybrid optimization algorithm. J Elect Eng Technol 9(3):859–865CrossRefGoogle Scholar
  6. 6.
    Umadevi N, Balaji M, Kamaraj V, Padmanaban LA (2015) Data interpolation and design optimisation of brushless DC motor using generalized regression neural network. J Elect Eng Technol 10(1):188–194CrossRefGoogle Scholar
  7. 7.
    Lim DK, Woo DK, Yeo HK, Jung SY, Ro JS, Jung HK (2015) A novel surrogate-assisted multi-objective optimization algorithm for an electromagnetic machine design. IEEE Trans Magn 51(3):1–4 (Art. ID 8200804) CrossRefGoogle Scholar
  8. 8.
    Lim DK, Yi KP, Jung SY, Jung HK, Ro JS (2015) Optimal design of an interior permanent magnet synchronous motor by using a new surrogate-assisted multi-objective optimization. IEEE Trans Magn 51(11):1–4 (Art. ID 8207504) Google Scholar
  9. 9.
    Lim DK, Yi KP, Jung SY, Jung HK (2018) A novel sequential-stage optimization strategy for an interior permanent magnet synchronous generator design. IEEE Trans Ind Electron 65(2):1781–1790CrossRefGoogle Scholar
  10. 10.
    Xia B, Ren Z, Zhang Y, Koh CS (2014) An adaptive optimization algorithm based on kriging interpolation with spherical model and its application to optimal design of switched reluctance motor. J Elect Eng Technol 9(5):1544–1550CrossRefGoogle Scholar
  11. 11.
    Lim D, Jin Y, Ong Y-S, Sendhoff B (2010) Generalizing surrogateassisted evolutionary computation. IEEE Trans Evol Comput 14(3):329–355CrossRefGoogle Scholar
  12. 12.
    Deb K, Pratap A, Agarwal S, Eyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol 6(2):182–197CrossRefGoogle Scholar
  13. 13.
    Coello CAC, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evol 8(3):256–278CrossRefGoogle Scholar
  14. 14.
    Son YD, Kang GH (2010) Drive system design for a permanent magnet motor with independent excitation winding for an electric bicycle. J Elect Eng Technol 5(4):623–630CrossRefGoogle Scholar
  15. 15.
    Hsu RC, Liu C-T, Chan D-Y (2012) A reinforcement-learningbased assisted power management with QoR provisioning for human-electric hybrid bicycle. IEEE Trans Ind Electron 59(8):3350–3359CrossRefGoogle Scholar
  16. 16.
    Chan TF, Yan LT, Fang SY (2002) In-wheel permanent-magnet brushless DC motor drive for an electric bicycle. IEEE Trans Energy Convers 17(2):229–233CrossRefGoogle Scholar
  17. 17.
    Lim DK, Cho YS, Ro JS, Jung SY, Jung HK (2016) Optimal design of an axial flux permanent magnet synchronous motor for the electric bicycle. IEEE Trans Magn 52(3):1–4 (Art. ID 8201204) Google Scholar
  18. 18.
    Xia C, Deng W, Shi T, Yan Y (2016) Torque ripple minimization of PMSM using parameter optimization based iterative learning control. J Elect Eng Technol 11(2):425–436CrossRefGoogle Scholar
  19. 19.
    Lee SH, Kim YJ, Lee KS, Kim SJ (2016) Multiobjective optimization design of small-scale wind power generator with outer rotor based on Box-Behnken design. IEEE Trans Appl Supercon 26(4):1–5 (Art. ID 5202605) Google Scholar
  20. 20.
    Xia C, Deng W, Shi T, Yan Y (2017) A novel method of reducing the cogging torque in SPM machine with segmented stator. J Elect Eng Technol 12(2):718–725CrossRefGoogle Scholar

Copyright information

© The Korean Institute of Electrical Engineers 2019

Authors and Affiliations

  1. 1.Department of Electrical EngineeringUniversity of UlsanUlsanSouth Korea
  2. 2.Department of Electrical EngineeringYeungnam UniversityGyeongbukSouth Korea

Personalised recommendations