Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
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Analysis of Neural Network Approaches for Nonlinear Modeling of Switched Reluctance Motor Drive

  • Saravanan, P (Dept. of Electrical and Electronics Engineering, SSN College of Engineering) ;
  • Balaji, M (Dept. of Electrical and Electronics Engineering, SSN College of Engineering) ;
  • Balaji, Nagaraj K (Dept. of Electrical and Electronics Engineering, SSN College of Engineering) ;
  • Arumugam, R (Dept. of Electrical and Electronics Engineering, SSN College of Engineering)
  • Received : 2017.01.07
  • Accepted : 2017.05.02
  • Published : 2017.07.01
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Abstract

This paper attempts to employ and investigate neural based approaches as interpolation tools for modeling of Switched Reluctance Motor (SRM) drive. Precise modeling of SRM is essential to analyse the performance of control strategies for variable speed drive application. In this work the suitability of Generalized Regression Neural Network (GRNN) and Extreme Learning Machine (ELM) in addition to conventional neural network are explored for improving the modeling accuracy of SRM. The neural structures are trained with the data obtained by modeling of SRM using Finite Element Analysis (FEA) and the trained neural network is incorporated in the model of SRM drive. The results signify the modeling accuracy with GRNN model. The closed loop drive simulation is performed in MATLAB/Simulink environment and the closeness of the results in comparison with the experimental prototype validates the modeling approach.

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References

  1. Khwaja M. Rahman, and Steven E. Schulz, IEEE Trans. Ind. Appl. 6, 38 (2002).
  2. Saurabh P. Nikam, Vandana Rallabandi, and B. G. Fernandes, IEEE Trans. Ind. Appl. 6, 48 (2012).
  3. Jaehyuch Kim, Yong-hoe Jeong, Yong-Hee Jeon, Jun-Ho Kang, Seunghun Lee, Jang-Yeop Park, J. Magnetics 3,19 (2014).
  4. Heon-Hyeong Lee, Qi Wang, Se-Joo Kim, Woongchul Choi, Geun-Ho Lee, J. Magnetic 3, 17 (2012).
  5. P. Chancharoensook, IEEE Power Electronics and Drive Systems, pp: 770-777 (2009).
  6. Bosra, M. Malaknezhad, M. Saniei, The 19th Iranian Conference of Electrical Engineering (ICEE), pp. 17-19, Tehran, Iran, (2011).
  7. J. Liang, D.H. Lee and J.W. Ahn, IET Electric Power Appl. 4, 3 (2009).
  8. J. Xiu, C.L. Xia, Trans. of China Electrotechnical Society 11, 22 (2007).
  9. F. Soares and P. J. C. Branco, IEEE Trans. Aero-Space Electron. Syst. 3, 37 (2001).
  10. H. J. Chen,D. Q. Jiang, J. Yang, and L.X. Shi, IEEE Trans. on Magnetics 8, 45 (2009).
  11. S. H. Mao, D. Dorrell, and M. C. Tsai, IEEE Trans. on Magnetics 7, 45 (2009).
  12. A. V. Radun, IEEE Trans. on Magnetics 36,4 (2000) https://doi.org/10.1109/20.824417
  13. A. Khalil and I. Husain, IEEE Trans. Ind. Appl. 3, 43 (2007).
  14. Ismael El-Samahy, Mostafa I. Marei and Ehab F. El-Saadany, Journal of Electrical Engineering & Technology, 3, pp. 68-78 (2008). https://doi.org/10.5370/JEET.2008.3.1.068
  15. S. Manolas, A. G. Kladas, and S. N. Manias, IEEE Trans. Magnetics 3, 45 (2009).
  16. Z. Y. Liu, S. S. Wang, and Z. Q. Deng, Proceedings of International Conference Electrical Machines and Systems, Wuhan, China, pp. 439-443 (2008)
  17. Keunsoo Ha, Jaehyuck Kim, Jang Young Choi, J. Magnetics 1, 19 (2014).
  18. D. Liang W. Ding, IET Electric Power Appl. 3, 3 (2009).
  19. Zhengyu Lin, Donald S. Reay, Barry W. Williams, and Xiangning He, IEEE Trans. Ind. Elec. 6, 54 (2007)
  20. GDS Hou Likun, Yang Qingxin, and An Jinlong, IEEE Trans. App. Superconductivity 3, 20 (2010).
  21. Jebarani Evangeline Stephen, Selvaraj Suresh Kumar, Jayaraj Jayakumar, Acta Polytechnica Hungarica 6, 11 (2014).
  22. Park, I. W. Sandberg, Neural Computations 3, pp. 246-257 (1991). https://doi.org/10.1162/neco.1991.3.2.246
  23. R. Jayapragash and C. Chellamuthu, Journal of Electrical Engineering & Technology, 10, pp. 990-1001 (2015). https://doi.org/10.5370/JEET.2015.10.3.990
  24. Perlovsky LI. Neural networks and intellect. Oxford University, Press; 2001.
  25. A. K. Jain, Jianchang Mao, K. M. Mohiuddin, Computer 3, 29 (1996).
  26. Lusu Guo, Leila Parsa, IEEE Trans. Ind. Elec. 3, 59 (2012).
  27. S. C. Huang, Y. F. Huang, IEEE Con. Sys. Magazine 3, 10 ( 1990).
  28. Celikoglu HB, Cigizoglu HK, Advanced Eng. Soft. 38, (2007).
  29. Cigizoglu HK, Alp M, Advanced Eng. Soft. 37:63-8, (2005).
  30. Kim B, Lee DW, Parka KY, Choi SR, Choi S, Vaccum 76, (2004).
  31. D. F. Specht, IEEE Trans. Neural Network 6, 2 (1991).
  32. H. Husain, M. Khalid, and R. Yusof, Proc. IEEE Region 10 Conf. (TENCON), 2, (2004).
  33. Weizhong Yan, IEEE Trans.Neural Net. and lear. Sys. 7, 23 (2012).
  34. N. Umadevi, M. Balaji, V. Kamaraj and L. Ananda Padmanaban, Journal of Electrical Engineering & Technology, 10, pp.188-194 (2015). https://doi.org/10.5370/JEET.2015.10.1.188
  35. Chao Wang, Xiao Liu, Hui Liu and Zhe Chen, Journal of Electrical Engineering & Technology, 11, pp.29-37 (2016). https://doi.org/10.5370/JEET.2016.11.1.029
  36. Guang-Bing Huang, Qin-Yu Zhu, Chee-Kheong Siew, Neurocomputing, 70 (2006).