Speed Control of Three Phase Induction Motor Drive Using Soft Computing Technique

  • Arunesh Kumar Singh
  • D. K. Chaturvedi
  • Jitendra Singh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 258)

Abstract

Variable speed AC Induction motors powered by switching power converters are becoming more and more popular, because of advances in solid state power devices, microprocessors and evaluation of soft computing technique. The most common principle of this kind, is the constant V/Hz principle. By making V/f constant, the magnitude of the magnetic field in the stator is kept at an approximately constant level throughout the operating range So three-phase induction motor drive systems driven by V/f (V/Hz) controlled PWM voltage source inverter have been widely used in the industrial applications. Soft Computing (SC) techniques are recently having significant impact on power electronics and motor drives, which is already a complex and multidisciplinary technology that is going through dynamic evolution in the recent years. Fuzzy Logic, Neural Networks, Neuro-Fuzzy and Evolutionary Computations are the core methodologies of soft computing (SC). In this paper, ANN controller and Fuzzy logic controller has been implemented for speed control of 3-phase induction motor by using soft computing techniques. Out of these two techniques ANN based control takes less settling time and has almost no overshoot. The result of the ANN controller is smoother than the fuzzy logic controller but its output can be improved by tuning it.

Keywords

Induction motor speed control Soft computing Fuzzy controller ANN controller 

References

  1. 1.
    Proca, A.B., Keyhani, A.: Identification of variable frequency induction motor models from operating data. IEEE Trans. Electrical Energy 17(1), 24–31 (2002)Google Scholar
  2. 2.
    Pinto, J.O.P., Bose, B.K., da Silva, L.E.B., Kazmierkowski, M.P.: A neural network based space vector PWM controller for voltage-fed inverter induction motor drive. IEEE Trans. Ind. Appl. 36, 1628–1636 (2000)CrossRefGoogle Scholar
  3. 3.
    Mondal, S.K., Pinto, J.O.P., Bose, B.K.: A neural network based space vector pwm controller for a three level voltage fed inverter induction motor drive. IEEE Trans. Ind. Appl. 38(3), 660–669 (2002)Google Scholar
  4. 4.
    Lee, Y.H., Suh, B.S., Hyun, D.S.: A novel PWM scheme for a three-level voltage source inverter with GTO thyristors. IEEE Trans. Ind. Appl. 32, 260–268 (1996)CrossRefGoogle Scholar
  5. 5.
    Koyama, M., Fujii, T., Uchida, R., Kawabata, T.: Space voltage vector based new PWM method for large capacity three-level GTO inverter. In: Proceedings IEEE IECON’92, pp. 271–276 (1992)Google Scholar
  6. 6.
    Luo, Y.-C., Chen, W.-X.: Sensorless stator field orientation controlled induction motor drive with a fuzzy speed controller. Comput. Math. Appl. 64(5), 1206–1216 (2012)CrossRefGoogle Scholar
  7. 7.
    Yager, R.G.: Fuzzy logics and artificial intelligence. J. Fuzzy Sets Syst. 90, 193–198 (1997)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Buhl, M., Lorenz, R.D.: Design and implementation of neural networks for digital current regulation of inverter drives. In: Conference Record IEEE-IAS Annual Meeting, pp. 415–421 (1991)Google Scholar
  9. 9.
    Singh, B., Bhuvaneswari, G., Garg, B.: Harmonic mitigation in AC–DC converters for vector controlled induction motor drives. IEEE Trans. Energy Convers. 22(3), 637–646 (2007)CrossRefGoogle Scholar
  10. 10.
    Xiang-Dong, S., Kang-Hoon, K., Byung-Gyu, Y., Matsui, M.: Fuzzy- logic-based V/f control of an induction motor for a DC grid power leveling system using flywheel energy storage equipment. IEEE Trans. Ind. Electron. 56(8), 3161–3168 (2009)CrossRefGoogle Scholar
  11. 11.
    Marcelo Suetake, I.N., da Silva, A.G.: Embedded DSP-based compact fuzzy system and its application for induction-motor V/f speed control. IEEE Trans. Ind. Electron. 58(3), 750–760 (2011)Google Scholar
  12. 12.
    Wang, Y., Song, J., Zhang, B.: Fuzzy sliding-mode variable structure control for fan filter units motor speed regulation system. Procedia Eng. 15, 969–973 (2011)CrossRefGoogle Scholar
  13. 13.
    Ustun, S.V., Demirtas. M: Modeling & control of V/f controlled induction motor using genetic-ANFIS algorithm. Energy Convers. Manage. 50, 786–791 (2009)Google Scholar
  14. 14.
    Tsai-Jiun Ren, T.-C.C.: Robust speed controlled induction motor drive based on recurrent neural network. Electr. Power Syst. Res. 76(12), 1064–1074 (2006)CrossRefGoogle Scholar
  15. 15.
    Tsai, C.-H., Yeh, M.-F.: Application of CMAC neural network to the control of induction motor drives. Appl. Soft Comput. 9(4), 1187–1196 (2009)Google Scholar
  16. 16.
    Bose, B.K.: Power Electronic and Drives, pp. 264–276. Prentice-Hall, Englewood CliMs (1986)Google Scholar
  17. 17.
    Gao, X.Z., Gao, X.M., Ovaska, S.J.: A modified Elman neural network model with application to dynamical system identification. In: Proceeding of the IEEE International Conference on Systems, Man and Cybernetics, pp. 1376–1381. Beijing, China (1996)Google Scholar
  18. 18.
    Lima, F., Kaiser, W., Nunes da Silva I., de Oliveira, A.A.: Speed neuro-fuzzy estimator applied to sensorless induction motor control. Latin Am. Trans. IEEE (Revista IEEE America Latina), 10, 2065–2073 (2012)Google Scholar
  19. 19.
    Oguz, Y., Dede, M.: Speed estimation of vector controlled squirrel cage asynchronous motor with artificial neural networks. Energy Convers. Manage. 52(1), 675–686 (2011)Google Scholar
  20. 20.
    Haykin, S.: Neural Networks. A Comprehensive Foundation. Macmillan Collage Publishing company, Inc., New York (1994)Google Scholar
  21. 21.
    Kim, S.-M., Han, W.-Y.: Induction motor servo drive using robust PID-like neuro-fuzzy controller. Control Eng. Pract. 14(5), 481–487 (2006)CrossRefGoogle Scholar
  22. 22.
    Ye, Z., Sadeghian, A., Wu, B.: Mechanical fault diagnostics for induction motor with variable speed drives using adaptive neuro-fuzzy inference system. Electr. Power Syst. Res. 76(9–10), 742–752 (2006)CrossRefGoogle Scholar
  23. 23.
    Lee, C.C.: Fuzzy logic in control systems: fuzzy logic controller—Part I, II. IEEE Trans. Syst. Man Cybern. 20(2), 404–435 (1990)Google Scholar
  24. 24.
    Limongi, L.R., Bojoi, R., Griva, G., Tenconi, A.: Performance comparison of DSP-based current controllers for three-phase active power filters. In: Proceedings of IEEE International Symposium on Industrial Electronic, pp. 136–141 (2008)Google Scholar
  25. 25.
    Al-Odienat, A.I., Al-Lawama, A.A.: The advantages of PID fuzzy controllers over the conventional types. Am. J. Appl. Sci. 5(6), 653–658 (2008)CrossRefGoogle Scholar
  26. 26.
    Karanayil, B., Rahman, M.F., Grantham, C.: Online stator and rotor resistance estimation scheme using artificial neural networks for vector controlled speed sensorless induction motor drive. IEEE Trans. Ind. Electron. 54(1), 167–176 (2007)CrossRefGoogle Scholar
  27. 27.
    Bose, B.K.: Modern Power Electronics and AC Drives. Prentice-Hall, Upper Saddle River (2002)Google Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  • Arunesh Kumar Singh
    • 1
  • D. K. Chaturvedi
    • 2
  • Jitendra Singh
    • 3
  1. 1.Jamia Millia IslamiaNew DelhiIndia
  2. 2.Dayalbagh Educational InstituteAgraIndia
  3. 3.IEC-CETNoidaIndia

Personalised recommendations