Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
권호 표지
DOI QR코드

DOI QR Code

Robust Optimization with Static Analysis Assisted Technique for Design of Electric Machine

  • Lee, Jae-Gil (Dept. of Electrical and Computer Engineering, Seoul National University) ;
  • Jung, Hyun-Kyo (Dept. of Electrical and Computer Engineering, Seoul National University) ;
  • Woo, Dong-Kyun (Dept. of Electrical Engineering, Yeungnam University)
  • Received : 2017.12.11
  • Accepted : 2018.05.26
  • Published : 2018.11.01
Download PDF
⟨ Previous Next ⟩

Abstract

In electric machine design, there is a large computation cost for finite element analyses (FEA) when analyzing nonlinear characteristics in the machine Therefore, for the optimal design of an electric machine, designers commonly use an optimization algorithm capable of excellent convergence performance. However, robustness consideration, as this factor can guarantee machine performances capabilities within design uncertainties such as the manufacturing tolerance or external perturbations, is essential during the machine design process. Moreover, additional FEA is required to search robust optimum. To address this issue, this paper proposes a computationally efficient robust optimization algorithm. To reduce the computational burden of the FEA, the proposed algorithm employs a useful technique which termed static analysis assisted technique (SAAT). The proposed method is verified via the effective robust optimal design of electric machine to reduce cogging torque at a reasonable computational cost.

Keywords

References

  1. Joseph V H. Beyer, B. Sendhoff, "Robust Optimization - A Comprehensive Survey," Comput. Methods Appl. Mech. Eng., vol. 196, no. 33-34, pp. 3190-3218, July, 2007. https://doi.org/10.1016/j.cma.2007.03.003
  2. P. Alotto, C. Magele, W. Renhart, G. Steiner, and A. Weber, "Robust target functions in electromagnetic design," Int. J. Comput. Math. Electr. Electron. Eng. (COMPEL), vol. 22, no. 3, pp. 549-560, 2003. https://doi.org/10.1108/03321640310475029
  3. Z. Ren, M. Pham, and C. Koh, "Robust Global Optimization of Electromagnetic Devices With Uncertain Design Parameters: Comparison of the Worst Case Optimization Methods and Multiobjective Optimization Approach Using Gradient Index," IEEE Trans. Magn., vol. 49, no. 2, pp. 851-859, Mar., 2013. https://doi.org/10.1109/TMAG.2012.2212713
  4. Gaspar-Cunha, and J. A. Covas, "Robustness in multiobjective optimization using evolutionary algorithms," Computational Optimization and Applications, vol. 39, no. 1, pp.75-96, Sept., 2015. https://doi.org/10.1007/s10589-007-9053-9
  5. Jae-Gil Lee, Nae-Won Hwang, Hee-rak Rue, Hyun-Kyo Jung, and Dong-Kyun Woo, "A new robust optimization approach applied to permanent magnet synchronous motor," IEEE Trans. Magn., vol. 53, no. 6, June, 2017.
  6. G. Lei, J. G. Zhu, Y. G. Guo, J. F. Hu, W, Xu, and K. R. Shao, "Robust design optimization of PM-SMC motors for six sigma quality manufacturing," IEEE Trans. Magn., vol. 49, no. 7, pp. 3953-3956, Jul. 2013. https://doi.org/10.1109/TMAG.2013.2243123
  7. Min Li, Rodrigo Silva, Federico Guimaraes, and David Lowther, "A New Robust Dominance Criterion for Multiobjective Optimization," IEEE Trans. Magn., vol. 51, no. 3, Mar., 2015.
  8. Song Xiao, Yingjiang Li, Mihai Rotaru, and Jan K. Sykulski, "Six Sigma Quality Approach to Robust Optimization," IEEE Trans. Magn., vol. 51, no. 3, June, 2015.
  9. Xiangjun Meng, Shuhong Wang, Jie Qiu, Qiuhui Zhang, Jian Guo Zhu, Youguang Guo, and Dikai Liu, "Robust Multilevel Optimization of PMSM Using Design for Six Sigma," IEEE Trans. Magn., vol. 47, no. 10, Oct., 2011.
  10. Gang Lei, J. G. Zhu, Y. G. Guo, J. F. Hu, Wei Xu, and K. R. Shao, "Robust Design Optimization of PMSMC Motors for Six Sigma Quality Manufacturing," IEEE Trans. Magn., vol. 49, no. 7, July, 2013.
  11. Z. Zhu, and D. Howe, "Influence of Design Paraeters on Cogging Torque in Permanent Magnet Machines," IEEE Trans. Energy Conversion., vol. 15, no. 4, pp. 407-412, Dec., 2000. https://doi.org/10.1109/60.900501
  12. I. Coenen, M. van der Giet, and K. Hameyer, "Manufacturing Tolerances: Estimation and Prediction of Cogging Torque Influenced by Magnetization Faults," IEEE Trans. Magn., vol. 48, no. 5, pp. 1932-1936, May, 2012. https://doi.org/10.1109/TMAG.2011.2178252
  13. L. Gasparin, A. Cernigoj, S. Markic, and R. Fiser, "Additional Cogging Torque Components in Permanent-Magnet Motors Due to Manufacturing Imperfections," IEEE Trans. Magn., vol. 45, no. 3, pp. 1210-1213, May, 2009. https://doi.org/10.1109/TMAG.2009.2012561
  14. A. J. P. Ortega, L. Xu, "Analytical Prediction of Torque Ripple in Surface-Mounted Permanent Magnet Motors Due to Manufacturing Variations," IEEE Trans. Energy Conversion, vol. 31, no. 4, pp. 1634-1644, Dec., 2016. https://doi.org/10.1109/TEC.2016.2598649
  15. L. Gasparin, A. Cernigoj, S. Markic, R. Fiser, "Prediction of Cogging Torque Level in PM Motors Due to Assembly Tolerances in Mass-Production", COMPEL, The Int. J. Comput. Mat. Elect. Electron. Eng., vol. 27, no.4, pp. 911-918, 2008. https://doi.org/10.1108/03321640810878333
  16. M. A. Khan, I. Husain, M. R. Islam, J. T. Klass, "Design of Experiments to Address Manufacturing Tolerances and Process Variations Influencing Cogging Torque and Back EMF in the Mass Production of the PMSMs," IEEE Trans. Industry Applications, vol. 50, no. 1, pp. 346-355, Jan/Feb., 2014. https://doi.org/10.1109/TIA.2013.2271473
  17. J. Chun, M. Kim, and H. Jung, "Shape Optimization of Electromagnetic Devices Using Immune Algorithm," IEEE Trans. Magn., vol. 33, no. 2, pp. 1876-1879, Mar., 1997. https://doi.org/10.1109/20.582650
  18. Feng Wang, Dexian Zhang, and Lichun Man, "Comparison of Immune and Genetic Algorithmns for Parameter Optimization of Plate Color Recognition," Progress in Informatics and Computing, IEEE International Conf, 2010.