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/content/aip/journal/adva/4/4/10.1063/1.4873535
1.
1. K. Vijayakumar, R. Karthikeyan, S. Paramasivam et al., “Switched Reluctance Motor Modeling, Design, Simulation, and Analysis: A Comprehensive Review,” IEEE Transactions on Magnetics 44(12), 46054617 (2008).
http://dx.doi.org/10.1109/TMAG.2008.2003334
2.
2. Shoujun Song, Weiguo Liu, D. Peitsch, and U. Schaefer, “Detailed design of a high speed switched reluctance starter/generator used in the more/all electric aircraft,” Chinese Journal of Aeronautics 23(2), 216226 (2010).
http://dx.doi.org/10.1016/S1000-9361(09)60208-9
3.
3. Z. Q. Zhu and C. C. Chan, “Electrical Machine Topologies and Technologies for Electric, Hybrid, and Fuel Cell Vehicles,” IEEE Vehicle Power and Propulsion Conference, pp. 232236, 2008.
4.
4. R. Cardenas, R. Pena, M. Perez et al., “Control of a Switched Reluctance Generator for Variable-Speed Wind Energy Applications,” IEEE Transactions on Energy Conversion 20(4), 781791 (2005).
http://dx.doi.org/10.1109/TEC.2005.853733
5.
5. J. Hur, C. Kim, and D. Hyun, “Modeling of Switched Reluctance Motor Using Fourier Seriesfor Performance Analysis,” Journal of Applied Physics 93(10), 87818783 (2003).
http://dx.doi.org/10.1063/1.1556987
6.
6. Y. Cai and C. Gao, “Nonlinear Modeling of Switched Reluctance Motor Based on BP Neural Network,” Third International Conference on Natural Computation, pp. 232236, 2007.
7.
7. R. Zhong, Y. P. Cao, W. Hua et al., “An Improved Model of Switched Reluctance Motors Based on Least Square Support Vector Machine,” IEEE International Symposium on Industrial Electronics, pp. 16, 2013.
8.
8. W. Ding and D. Liang, “Modeling of a 6/4 Switched Reluctance Motor Using Adaptive Neural Fuzzy Inference System,” IEEE Transactions on Magnetics 44(7), 17961804 (2008).
http://dx.doi.org/10.1109/TMAG.2008.919711
9.
9. X. Zan and F. Xie, “Study on Simulation Model of Switched Reluctance Starter/Generator System Based on Wavelet Neural Network,” International Conference on Advanced Mechatronic Systems, pp. 118122, 2011.
10.
10. P. Cristea, R. Tuduce, and A. Cristea, “Time series prediction with wavelet neural networks,” 5th Seminar on Neural Network Applications in Electrical Engineering, pp. 510, 2000.
11.
11. C. Guan, P. B. Luh, L. D. Michel, Y. T. Wang, and P. B. Friedland, “Very Short-Term Load Forecasting: Wavelet Neural Networks with Data Pre-Filtering,” IEEE Transactions on Power Systems 28(1), 3041 (2013).
http://dx.doi.org/10.1109/TPWRS.2012.2197639
12.
12. W. Puchalski, F. Fidelis Schauenburg, V. Cocco Mariani, and L. Dos Santos Coelho, “Wavelet neural network approach applied to biomechanics of swimming,” 13th UK Workshop on Computational Intelligence, pp. 214220, 2013.
13.
13. Fathy El Sayed Abdel-Kader, M. Z. Elsherif, Naser M. B. Abdel-Rahim, and Mohamed M. Fathy, “Control methods of the switched reluctance motor in electric vehicle during acceleration,” Journal of Renewable and Sustainable Energy 4, 063142 (2012).
http://dx.doi.org/10.1063/1.4772964
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/content/aip/journal/adva/4/4/10.1063/1.4873535
2014-04-24
2016-09-27

Abstract

According to the strong nonlinear electromagnetic characteristics of switched reluctance machine (SRM), a novel accurate modeling method is proposed based on hybrid trained wavelet neural network (WNN) which combines improved genetic algorithm (GA) with gradient descent (GD) method to train the network. In the novel method, WNN is trained by GD method based on the initial weights obtained per improved GA optimization, and the global parallel searching capability of stochastic algorithm and local convergence speed of deterministic algorithm are combined to enhance the training accuracy, stability and speed. Based on the measured electromagnetic characteristics of a 3-phase 12/8-pole SRM, the nonlinear simulation model is built by hybrid trained WNN in Matlab. The phase current and mechanical characteristics from simulation under different working conditions meet well with those from experiments, which indicates the accuracy of the model for dynamic and static performance evaluation of SRM and verifies the effectiveness of the proposed modeling method.

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