Software Environment for Online Simulation of Switched Reluctance Machines

  • Virgil ChindrişEmail author
  • Rareş Terec
  • Mircea Ruba
  • Loránd Szabó
Part of the Studies in Computational Intelligence book series (SCI, volume 416)


Simulations are widely used in the study of both electrical machines and drives. In the literature several simulation programs can be found, which can also be applied in the study of the switched reluctance motors (SRMs). The real-time simulation tool developed by the authors is a novel approach as it enables also online simulations. The control of the SRM can be performed from the computer’s keyboard (by energizing/un-energizing each coil from its corresponding key), from a built-in controller or from an external one, which can be connected via TCP/IP protocol. The program enables parameter changing and instantaneous value displaying during the simulations. The studied machine’s characteristics can be plotted real-time, or after finishing the simulations. All the obtained results can be saved in external files for future data processing. The software tool proved to be very useful both in checking the design of a SRM, but also in setting up and verifying its proper control strategy. The online simulation program can also be practical in teaching electrical machines and drives, and it can be extended to be used in remote laboratories, too.


Electrical Machine Simulation Program Phase Current Software Environment Rotor Position 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Developer Studio 2006 Reference. Delphi Language Guide, C++ Language Guide, Together Reference. Borland Software Corporation, Scotts Valley, USA (2006)Google Scholar
  2. 2.
    Adurariu, E.P., San, L.S., Viorel, I.A., Tiş, C.M., Cornea, O.: Switched reluctance motor analytical models, comparative analysis. In: Proceedings of the 12th International Conference on Optimization of Electrical and Electronic Equipment (OPTIM 2010), Moieciu, Romania (2010)Google Scholar
  3. 3.
    Aldrich, C.: Learning online with games, simulations, and virtual worlds: Strategies for online instruction. Jossey-Bass Guides to Online Teaching and Learning. John Wiley and Sons, New York (2009)Google Scholar
  4. 4.
    Asghari, B., Dinavahi, V.: Permeance network based real-time induction machine model. In: Proceedings of the International Conference on Power Systems Transients (IPST 2009), Kyoto, Japan, pp. 1–6 (2009)Google Scholar
  5. 5.
    Bauer, P., van Duijsen, P.J.: Challenges and advances in simulation. In: Proceedings of the IEEE 36th Power Electronics Specialists Conference (PESC 2005), Recife, Brazil, pp. 1030–1036 (2005)Google Scholar
  6. 6.
    Bauer, P., Fedak, V., Hajek, V., Lampropoulos, I.: Survey of distance laboratories in power electronics. In: Proceedings of the 39th IEEE Power Electronic Specialists Conference (PESC 2008), Rhodes, Greece, pp. 430–436 (2008)Google Scholar
  7. 7.
    Bauer, P., Korondi, P., van Duijsen, P.J.: Integrated control — simulation design approach. In: Proceedings of the International Conference on Power Electronics, Drives and Motion (PCIM 2003), Nürnberg, Germany (2003)Google Scholar
  8. 8.
    Cantù, M.: Mastering Borland Delphi 2005. SYBEX, Indianapolis (2006)Google Scholar
  9. 9.
    Chindriş, V., Szász, C.: Real–time simulation of embryonic structures for high reliability mechatronic applications. In: Proceedings of the 16th International Conference on Building Services, Mechanical and Building Industry Days, Debrecen, Hungary, pp. 571–578 (2010)Google Scholar
  10. 10.
    Chindriş, V., Terec, R., Ruba, M., Szabó, L., Rafajdus, P.: Useful software tool for simulating switched reluctance motors. In: Proceedings of the 25th European Conference on Modelling and Simulation (ECMS 2011), Kraków, Poland, pp. 216–221 (2011)Google Scholar
  11. 11.
    D’hulster, F.: Switched reluctance motor (SRM) drive modelling using flux to simulink technology. Flux magazine 41, 10–11 (2003)Google Scholar
  12. 12.
    Ding, W., Liang, D.: A fast analytical model for an integrated switched reluctance starter/generator. IEEE Transactions on Energy Conversion 25(4), 948–956 (2010)CrossRefGoogle Scholar
  13. 13.
    Henao, H., Capolino, G.A., Poloujadoff, M., Bassily, E.: A new control angle strategy for switched reluctance motor. In: Proceedings of the 7th European Conference on Power Electronics and Applications, Trondheim, Norway, pp. 613–618 (1997)Google Scholar
  14. 14.
    Henneberger, G., Viorel, I.A.: Variable Reluctance Electrical Machines. Shaker Verlag, Aachen (2001)Google Scholar
  15. 15.
    Ichinokura, O., Onda, T., Kimura, M., Watanabe, T., Yanada, T., Guo, H.: Analysis of dynamic characteristics of switched reluctance motor based on SPICE. IEEE Transactions on Magnetics 34(4), 2147–2149 (1998)CrossRefGoogle Scholar
  16. 16.
    Ichinokura, O., Suyama, S., Watanabe, T., Guo, H.J.: A new calculation model of switched reluctance motor for use on SPICE. IEEE Transactions on Magnetics 37(4), 2834–2836 (2001)CrossRefGoogle Scholar
  17. 17.
    Khalil, A., Husain, I.: A fourier series generalized geometry-based analytical model of switched reluctance machines. IEEE Transactions on Industry Applications 43(3), 673–684 (2007)CrossRefGoogle Scholar
  18. 18.
    Kozierok, C.M.: The TCP/IP guide: a comprehensive, illustrated Internet protocols reference. No Starch Press, San Francisco (2005)Google Scholar
  19. 19.
    Krishnan, R.: Switched reluctance motor drives: modeling, simulation, analysis, design, and applications. CRC, Boca Raton (2001)CrossRefGoogle Scholar
  20. 20.
    Lachman, T., Mohamad, T., Fong, C.: Nonlinear modelling of switched reluctance motors using artificial intelligence techniques. IEE Proceedings — Electric Power Applications 151(1), 53–60 (2004)CrossRefGoogle Scholar
  21. 21.
    Lu, W., Keyhani, A., Fardoun, A.: Neural network-based modeling and parameter identification of switched reluctance motors. IEEE Transactions on Energy Conversion 18(2), 284–290 (2003)CrossRefGoogle Scholar
  22. 22.
    Miller, F.P., Vandome, A.F., McBrewster, J.: Graphical User Interface. VDM Publishing House Ltd., Beau Bassin (2009)Google Scholar
  23. 23.
    Miller, T.J.E.: Switched Reluctance Motors and Their Control. Magna Physics (1993)Google Scholar
  24. 24.
    Miller, T.J.E.: Electronic Control of Switched Reluctance Machines. Newnes, Oxford (2001)Google Scholar
  25. 25.
    Mohan, M., Undeland, T.M., Robbins, W.P.: Power Electronics: Converters, Applications and Design. John Wiley and Sons, New York (2003)Google Scholar
  26. 26.
    Moreira, J.C., Lipo, T.: Simulation of a four phase switched reluctance motor including the effects of mutual coupling. Electric Machines and Power Systems 16(4), 281–299 (1989)CrossRefGoogle Scholar
  27. 27.
    O’Dwyer, J., Vonhof, E.: Saturable variable reluctance motor simulation using spline functions. In: Proceedings of the International Conference on Electrical Machines (ICEM 1994), Paris, France (1994)Google Scholar
  28. 28.
    Ong, C.M.: Dynamic simulation of electric machinery: using Matlab/Simulink. Prentice Hall PTR, Upper Saddle (1998)Google Scholar
  29. 29.
    Perl, J.: Antagonistic adaptation systems: An example of how to improve understanding and simulation complex system behaviour by use of meta-models and on line-simulation. In: Proceedings of the 16th International Conference on Scientific Computing & Mathematical Modelling (IMACS 2000), Laussane, Switzerland (2000)Google Scholar
  30. 30.
    Preston, M.A., Lyons, J.P.: A switched reluctance motor model with mutual coupling and multi-phase excitation. IEEE Transactions on Magnetics 27(6), 5423–5425 (1991)CrossRefGoogle Scholar
  31. 31.
    Radun, A.: Analytically computing the flux linked by a switched reluctance motor phase when the stator and rotor poles overlap. IEEE Transactions on Magnetics 36(4) (2000)Google Scholar
  32. 32.
    Ruba, M., Anders, M.: Fault tolerant switched reluctance machine study. In: Proceedings of the International Conference on Power Electronics, Intelligent Motion and Power Quality (PCIM 2008), Nürnberg, Germany (2008)Google Scholar
  33. 33.
    Soares, F., Branco, P.C.: Simulation of a 6/4 switched reluctance motor based on matlab/simulink environment. IEEE Transactions on Aerospace and Electronic Systems 37(3), 989–1009 (2001)CrossRefGoogle Scholar
  34. 34.
    Strete, L., Husain, I., Cornea, O., Viorel, I.A.: Direct and inverse analytical models of a switched reluctance motor. In: Proceedings of the 14th Biennial IEEE Conference on Electromagnetic Field Computation (CEFC 2010), Chicago, USA, pp. 1–6 (2010)Google Scholar
  35. 35.
    Szabó, L., Ruba, M.: Using co-simulations in fault tolerant machine’s study. In: 23rd European Conference on Modelling and Simulation (ECMS 2009), Madrid, Spain, pp. 756–762 (2009)Google Scholar
  36. 36.
    Szász, C., Chindriş, V.: Fault-tolerant embryonic network development for high reliability mechatronic applications. International Review of Applied Sciences and Engineering 1(1), 61–66 (2010)CrossRefGoogle Scholar
  37. 37.
    Szász, C., Chindriş, V., Szabó, L.: Modeling and simulation of embryonic hardware structures designed on FPGA–based artificial cell network topologies. In: Proceedings of the 23rd European Conference on Modelling and Simulation (ECMS 2009), Madrid, Spain (2009)Google Scholar
  38. 38.
    Torrey, D.A., Lang, J.H.: Modelling a nonlinear variable-reluctance motor drive. IEE Proceedings B — Electric Power Applications 137(5), 314–326 (1990)CrossRefGoogle Scholar
  39. 39.
    Tsukii, T., Nakamura, K., Ichinokura, O.: SPICE simulation of SRM considering nonlinear magnetization characteristics. Electrical Engineering in Japan, 50–56 (2003)Google Scholar
  40. 40.
    Ustun, O.: A nonlinear full model of switched reluctance motor with artificial neural network. Energy Conversion and Management 50(9), 2413–2421 (2009)CrossRefGoogle Scholar
  41. 41.
    Viorel, I.A., Forrai, A., Ciorba, R.C.: On the switched reluctance motor circuit-field model. In: Proceedings of the International Conference on Electrical Machines (ICEM 1996), Vigo, Spain, pp. 94–98 (1996)Google Scholar
  42. 42.
    Wen, D., Deliang, L., Zhuping, C.: Dynamic model and simulation for a 6/4 switched reluctance machine system assisted by maxwell SPICE and simplorer. In: Proceedings of the International Conference on Mechatronics and Automation (ICMA 2007), Harbin, China, pp. 1699–1704 (2007)Google Scholar
  43. 43.
    Wu, W., Dunlop, J.B., Collocott, S.J., Kalan, B.A.: Design optimization of a switched reluctance motor by electromagnetic and thermal finite-element analysis. IEEE Transactions on Magnetics 39(5), 3334–3336 (2003)CrossRefGoogle Scholar
  44. 44.
    Yao, R., Stiebler, M., Liu, D.: A generalized simulation for switched reluctance motor variable–speed system. In: Proceedings of the International Conference on Electrical Machines (ICEM 1998), Instanbul, Turkey, pp. 1686–1691 (1998)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  • Virgil Chindriş
    • 1
    Email author
  • Rareş Terec
    • 1
  • Mircea Ruba
    • 1
  • Loránd Szabó
    • 1
  1. 1.Department of Electrical Machines and DrivesTechnical University of ClujMemorandumuluiRomania

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