Torque and Flux Ripple Minimization of Induction Motor Using Hybrid Neuro Fuzzy Controller

  • Authors

    • V. Sakthi
    • A. Sivasubramanian
    2018-09-25
    https://doi.org/10.14419/ijet.v7i4.6.28445
  • Direct Torque Control (DTC), Field Oriented Control (FOC), Fuzzy Logic Controller (FLC), Induction Motor (IM), Neuro Fuzzy Controller (NFC).

  • This paper proposed to design, implementation and simulation of Direct Torque Control of Induction Motor drive system is to minimize stator current distortion, electromagnetic torque and flux ripples. In this paper, Hybrid Neuro-Fuzzy Controller is proposed to replace the conventional PI torque and flux controller to achieve desired torque and flux with zero steady state error and also with good dynamic fast response. Neuro-Fuzzy based torque and flux controllers are designed to optimize the stator voltages in d-q reference frame that applied to Direct Torque Control. Simulation and the performance of the proposed Hybrid NFC are analyzed. Simulation results showed that a significant improvement in dynamic torque and speed response in both steady and transient states and also a considerable reduction in torque and flux minimization compared to the other AI techniques.

     

     

  • References

    1. [1] Bose, B.K., 1996. Power electronics & variable frequency drives, IEEE pres, New York.

      [2] Takashi, I., T. Naguchi, 1986. “A new quickresponse and high-efficiency control strategy of induction motorâ€, IEEE Trans, On 22(5):820-827.

      [3] Takashi and Y. Ohmori, 1989. “High performance DTC of an induction motorâ€, IEEE Trans. On industry applications, 25(2): 14-18.

      [4] Pujol, A.A., 2000. “Improvement in direct torque control of induction motorsâ€, These de doctorat de L’UPC.

      [5] Toufouti, R., S. Meziane, H. Bemalla, 2006. “Direct torque control for induction motor using Fuzzy Logicâ€, ICGST Trans. On ACSE, 6(2): 17-24.

      [6] Mokrani, L., R. Abdessemed, 2003. A fuzzy self- tuning PI controller for speed control of induction motor drive, in: Proceedings of the IEEE Conference on control applications Piscataway, NJ, USA, pp: 785-790.

      [7] Lai, Y., J. Lin, 2003. New hybrid fuzzy controller for direct torque control of induction motor drives, IEEE Trans, Power Electron, 18:1211-1219.

      [8] Baburaj Karanayi, and Muhammed Fazlur Rahman, 2005. “Stator and Rotor resistance observers for induction motor drive using fuzzy logic and Artificial neural networks,†IEEE Trans. J.Energy Convers., 20(4): 771-780.

      [9] Jang, J. 1996. ANFIS: adaptive network-based fuzzy inference systems. IEEE, Trans. Syst. Nan cybern, 23: 665-85.

      [10] P. Vas, “Artificial-intelligence-based electrical machines and drivesâ€, New York: Oxford Univ. Press, (1999).

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  • How to Cite

    Sakthi, V., & Sivasubramanian, A. (2018). Torque and Flux Ripple Minimization of Induction Motor Using Hybrid Neuro Fuzzy Controller. International Journal of Engineering & Technology, 7(4.6), 377-381. https://doi.org/10.14419/ijet.v7i4.6.28445