The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
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A Magnet Pole Shape Optimization of a Large Scale BLDC Motor Using a RSM With Design Sensitivity Analysis

민감도기법과 RSM을 이용한 대용량 BLDC 전동기 영구자석의 형상 최적화

  • Published : 2009.04.01
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Abstract

This paper presents an algorithm for the permanent magnet shape optimization of a large scale BLDC(Brushless DC) motor to minimize the cogging torque. A response surface method (RSM) using multiquadric radial basis function is employed to interpolate the objective function in design parameter space. In order to get a reasonable response surface with relatively small number of sampling data points, additional sampling points are added on the basis of design sensitivity analysis computed by using FEM. The algorithm has 2 stages: the first stage is to determine the PM arc angle, and the 2nd stage is to optimize the magnet pole shape. The developed algorithm is applied to a 5MW BLDC motor to get a minimum cogging torque. After 3 iterations with 4 design parameters, the cogging torque is reduced to 13.2% of the initial one.

Keywords

References

  1. C. S. Koh, H. S. Yoon, K. W Nam, and H. S. Choi, 'Magnetic Pole Shape Optimization of Permanent Magnet Motor for Reduction of Cogging Torque', IEEE Trans. on Magnetics, vol. 33, no. 2, pp.1822-1827, March 1997 https://doi.org/10.1109/20.582633
  2. J. S. Ryu, Y. Yao, C. S. Koh and et. aI., 'Utilizing Design Sensitivity Analysis for the Global Optimization of Electromagnetic Devices with C1 Piecewise Response Surface Patches', IEEE Trans on Magn, vol. 41, no 5, pp. 1792-1795, May 2005 https://doi.org/10.1109/TMAG.2005.845982
  3. K. J. Han, H. S. Cho, D. H. Cho and H. K. jung, 'Optimal Core Shape Design for Cogging Torque Reduction of Brushless DC Motor Using Genetic Algorithm', IEEE Trans. on Magnetics, vol. 36, no. 4, pp. 1927-1931, July 2000 https://doi.org/10.1109/20.877824
  4. T. K. Chung, S. K. Kim, and S. Y. Hahn, 'Optimal Pole Shape Design for the Reduction of Cogging Torque of Brushless DC Motor Using Evolution Startegy', IEEE Trans. on Magnetics, vol. 33, no. 2, pp.1908-1911, March 1997 https://doi.org/10.1109/20.582662
  5. C. A. Borghi, D. Casadei, A. Cristofolini, M. Fabbri, and G. Serra, 'Application of a Multiobjective Minimization Technique for Reducing the Torque Ripple in Permanent 'Magnet Motors', IEEE Trans. on Magnetics, vol.35, no.5, pp.4238-4246, September 1999 https://doi.org/10.1109/20.799073
  6. X. K. Gao, T. S. Low, Z. J. Liu, and S. X. Chen, 'Robust Design for Torque Optimization Using Response Surface Methodology', IEEE Trans. on Magnetics, vol. 38, no. 2, pp. 1141-1144, March 2002 https://doi.org/10.1109/20.996292
  7. D. Tsao, and J. P. Webb, 'Construction of device performance models using adaptive interpolation and sensitivities', IEEE Trans. On Magnetics, vol. 41, no. 5, pp. 1768-1771, May 2005 https://doi.org/10.1109/TMAG.2005.845997
  8. S. Rippa, 'An algorithm for selecting a good value for the parameter c in radial basis function interpolation', Advances m Computational Mathematics, vol. 11, pp. 193-210, 1999 https://doi.org/10.1023/A:1018975909870
  9. S. M. Robinson, 'Electric Ship Propulsion', Simmons-Boardman Publishing Company, 1922
  10. Manfred Kasper, 'Shape Optimization by Evolution Strategy', IEEE Trans. on Magn., vol.28, no.2, pp.l556-1560, March 1992 https://doi.org/10.1109/20.123995
  11. 한국해양대학교, '수중운동체특화연구센터 보고서', 2007.12
  12. 大川光占 (역:원종수), '페라이트 磁石回轉機의 設計', 동일출판사 1995. 5
  13. J. R. Hendershot Jr., TJE Miller 'Design of Brushless Permanent-Magnet Motors', Magna Physics Publishing and Clarendon Press, Oxford, 1994
  14. T. Zhang, 'A leave-one-out cross validation bound for kernel methods with application in learning,' presented at the Conf. Computational Learning Theory, 2001 https://doi.org/10.1007/3-540-44581-1_28
  15. M. M. S. Lee, S. S. Keerthi, C. J. Ong, and Dennis DeCoste, 'An Efficient Method for Computing Leave-One-Out Error in Support Vector Machines With Gaussian Kernels', IEEE Trans. On Neural Networks, vol. 15, no. 3, pp. 750-757, MAY 2004 https://doi.org/10.1109/TNN.2004.824266