Optimal design of a 1 kW switched reluctance generator for wind power systems using a genetic algorithm

Hye-Ung Shin; Kyo-Beum Lee

Optimal design of a 1 kW switched reluctance generator for wind power systems using a genetic algorithm

View Fulltext

Author(s): Hye-Ung Shin ¹ and Kyo-Beum Lee ¹
- Affiliations: 1: Department of Electrical and Computer Engineering, Ajou University, San5, Woncheon-dong, Yeongtong-gu, Suwon 443-749, Korea
Source: Volume 10, Issue 8, September 2016, p. 807 – 817
DOI: 10.1049/iet-epa.2015.0582 , Print ISSN 1751-8660, Online ISSN 1751-8679

Received 26/11/2015, Accepted 11/05/2016, Revised 04/05/2016, Published 01/09/2016

This study presents an optimal design for a 1 kW switched reluctance generator (SRG) for wind-power applications. The design of the SRG is optimised to increase efficiency and reduce the volume of the generator compared with a basic model designed using the D²L method. To begin the optimisation, Latin hypercube sampling is employed to extract samples of the design variables for the design of experiment. The Kriging method of approximation modelling is used to interpolate the non-linear characteristics for the optimal design. Finally, a genetic algorithm is utilised to optimise the original design model for efficiency and reduced volume. The optimal design of the SRG uses the shape parameters of the basic model. Design variables are selected to be at their maxima. The designed optimal model is compared with the basic model and prototype to verify the results.

References

1. 1)
  - 25. Widmer, J.D., Mecrow, B.C.: ‘Optimized segmental rotor switched reluctance machines with a greater number of rotor segments than stator slots’, IEEE Trans. Ind. Appl., 2013, 49, (4), pp. 1491–1498 (doi: 10.1109/TIA.2013.2255574).
2. 2)
  - 22. Cardenas, R., Pena, R., Perez, M., et al: ‘Control of a switched reluctance generator for variable-speed wind energy applications’, IEEE Trans. Energy Convers., 2005, 20, (4), pp. 781–791 (doi: 10.1109/TEC.2005.853733).
3. 3)
  - 19. Kobetski, A., Coulomb, J.L., Costa, M.C., et al: ‘Comparison of radial basis function approximation techniques’, COMPEL, 2003, 22, (3), pp. 616–629 (doi: 10.1108/03321640310475074).
4. 4)
  - 11. Mirzaeian, B., Moallem, M., Tahani, V., et al: ‘Multi objective optimization method based on a genetic algorithm for switched reluctance motor design’, IEEE Trans. Magn., 2002, 38, (3), pp. 1524–1527 (doi: 10.1109/20.999126).
5. 5)
  - 2. Valenciaga, F., Puleston, P.F.: ‘High-order sliding control for a wind energy conversion system based on a permanent magnet synchronous generator’, IEEE Trans. Energy Convers., 2008, 23, (3), pp. 860–867 (doi: 10.1109/TEC.2008.922013).
6. 6)
  - 16. Simpson, T.W., Mauery, T.M., Korte, J., et al: ‘Kriging models for global approximation in simulation-based multidisciplinary design optimization’, AIAA, 2001, 39, (12), pp. 2233–2241 (doi: 10.2514/2.1234).
7. 7)
  - 24. Cheng, J., Druzdzel, M.J.: ‘Latin hypercube sampling in Bayesian networks’. Proc. Int. Conf. on FLAIRS, Austin, USA, July/August 2000, pp. 287–292.
8. 8)
  - 9. Byun, J.K., Park, I.H., Hahn, S.Y.: ‘Topology optimization of electrostatic actuator using design sensitivity’, IEEE Trans. Magn., 2002, 38, (2), pp. 1053–1056 (doi: 10.1109/20.996270).
9. 9)
  - 3. Lee, S.B., Lee, K.B., Lee, D.C., et al: ‘An improved control method for a DFIG in a wind turbine under an unbalanced grid voltage condition’, J. Electr. Eng. Technol., 2010, 5, (4), pp. 614–622 (doi: 10.5370/JEET.2010.5.4.614).
10. 10)
  - 32. Krishnan, R., Arumugan, R., Lindsay, J.F.: ‘Design procedure for switched-reluctance motors’, IEEE Trans. Ind. Appl., 1988, 24, (3), pp. 456–461 (doi: 10.1109/28.2896).
11. 11)
  - 24. Materu, P., Krishnan, R.: ‘Estimation of switched reluctance motor losses’, IEEE Trans. Ind. Appl., 1992, 28, (3), pp. 668–679 (doi: 10.1109/28.137456).
12. 12)
  - 14. Kim, J.B., Hwang, K.Y., Kwon, B.I.: ‘Optimization of two-phase in-wheel IPMSM for wide speed range by using the Kriging model based on Latin hypercube sampling’, IEEE Trans. Magn., 2011, 47, (5), pp. 1078–1081 (doi: 10.1109/TMAG.2010.2096409).
13. 13)
  - 1. Chwa, D.K., Lee, K.B.: ‘Variable structure control of the active and reactive powers for a DFIG in wind turbines’, IEEE Trans. Ind. Appl., 2010, 46, (6), pp. 2545–2555 (doi: 10.1109/TIA.2010.2073674).
14. 14)
  - 13. Byun, J.K., Lee, J.H., Park, I.H., et al: ‘Inverse problem application of topology optimization method with mutual energy concept and design sensitivity’, IEEE Trans. Magn., 2000, 36, (4), pp. 1144–1147 (doi: 10.1109/20.877643).
15. 15)
  - 13. Mendez, S., Martinez, A., Millan, W., et al: ‘Design, characterization, and validation of a 1-kW AC self-excited switched reluctance generator’, IEEE Trans. Ind. Electron., 2014, 61, (2), pp. 846–855 (doi: 10.1109/TIE.2013.2254098).
16. 16)
  - 10. Dyck, D.N., Lowther, D.A.: ‘Automated design of magnetic devices by optimizing material distribution’, IEEE Trans. Magn., 1996, 32, (3), pp. 1188–1193 (doi: 10.1109/20.497456).
17. 17)
  - 20. Mahmoudi, A., Kahourzade, S., Rahim, N.A., et al: ‘Design, analysis, and prototyping of an axial-flux permanent magnet motor based on genetic algorithm and finite-element analysis’, IEEE Trans. Magn., 2013, 49, (4), pp. 1479–1492 (doi: 10.1109/TMAG.2012.2228213).
18. 18)
  - 26. Sheth, N.K., Rajagopal, K.R.: ‘Optimum pole arcs for a switched reluctance motor for higher torque with reduced ripple’, IEEE Trans. Magn., 2003, 39, (5), pp. 3214–3216 (doi: 10.1109/TMAG.2003.816151).
19. 19)
  - 25. Davey, K.R.: ‘Latin hypercube sampling and pattern search in magnetic field optimization problems’, IEEE Trans. Magn., 2008, 44, (6), pp. 974–977 (doi: 10.1109/TMAG.2007.916292).
20. 20)
  - 12. Byun, J.K., Hahn, S.Y., Park, I.H.: ‘Topology optimization of electrical devices using mutual energy and sensitivity’, IEEE Trans. Magn., 1999, 35, (5), pp. 3718–3720 (doi: 10.1109/20.800642).
21. 21)
  - 28. Torkaman, H., Ebrahim, A.: ‘Comprehensive study of 2-D and 3-D finite element analysis of a switched reluctance motor’, J. Appl. Sci., 2008, 8, (15), pp. 2758–2763 (doi: 10.3923/jas.2008.2758.2763).
22. 22)
  - 23. Shin, P.S., Woo, S.H., Koh, C.S.: ‘An optimal design of large scale permanent magnet pole shape using adaptive response surface method with Latin hypercube sampling strategy’, IEEE Trans. Magn., 2009, 45, (3), pp. 1214–1217 (doi: 10.1109/TMAG.2009.2012565).
23. 23)
  - 8. Xue, X.D.K., Cheng, W.E., Bao, Y.J., et al: ‘Switched reluctance generators with hybrid magnetic paths for wind power generation’, IEEE Trans. Magn., 2012, 48, (11), pp. 3863–3866 (doi: 10.1109/TMAG.2012.2202094).
24. 24)
  - 21. Krishnan, R.: ‘Switched reluctance motor drives: modeling, simulation, analysis, design, and applications’ (CRC Press, Boca Raton, FL, USA, 2001), pp. 82–83.
25. 25)
  - 15. Hwang, C.C., Lyu, L.Y., Liu, C.T., et al: ‘Optimal design of an SPM motor using genetic algorithms and Taguchi method’, IEEE Trans. Magn., 2008, 44, (11), pp. 4325–4328 (doi: 10.1109/TMAG.2008.2001526).
26. 26)
  - 18. Kim, S., Bhan, J.H., Hong, J.P., et al: ‘Optimization technique for improving torque performance of concentrated winding interior PM synchronous motor with wide speed range’. Proc. 41st IAS Annual Meeting, Tampa, USA, October 2006, pp. 1933–1940.
27. 27)
  - 4. Kang, Y.K., Jeong, H.G., Lee, K.B., et al: ‘Simple estimation scheme for initial rotor position and inductances for effective MTPA-operation in wind-power systems using an IPMSM’, J. Power Electron., 2010, 10, (4), pp. 396–404 (doi: 10.6113/JPE.2010.10.4.396).
28. 28)
  - 12. Chen, J., Nayar, C.V., Xu, L.: ‘Design and finite-element analysis of an outer-rotor permanent-magnet generator for directly coupled wind turbines’, IEEE Trans. Magn., 2000, 36, (5), pp. 3802–3809 (doi: 10.1109/20.908378).

Login

Not registered yet?

Share

Tools

Login to add to favourites

Key

Optimal design of a 1 kW switched reluctance generator for wind power systems using a genetic algorithm

References

Related content