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Neural Network-Based Model Reference Adaptive System for Torque Ripple Reduction in Sensorless Poly Phase Induction Motor Drive

Author

Listed:
  • S. Usha

    (Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Kattankulathur 603203, India)

  • C. Subramani

    (Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Kattankulathur 603203, India)

  • Sanjeevikumar Padmanaban

    (Center for Bioenergy and Green Engineering, Department of Energy Technology, Aalborg University, 6700 Esbjerg, Denmark)

Abstract

This paper proposes the modified, extended Kalman filter, neural network-based model reference adaptive system and the modified observer technique to estimate the speed of a five-phase induction motor for sensorless drive. The proposed method is generated to achieve reduced speed deviation and reduced torque ripple efficiently. In inclusion, the result of speed performance and torque ripple under parameter variations were analysed and compared with the conventional direct synthesis method. The speed estimation of a five-phase motor in the four methods is analysed using MATLAB Simulink platform, and the optimum method is recognized using time domain analysis. It is observed that speed error is minimized by 60% and torque ripple is reduced by 75% in the proposed method. The hardware setup is carried out for the optimized method identified.

Suggested Citation

  • S. Usha & C. Subramani & Sanjeevikumar Padmanaban, 2019. "Neural Network-Based Model Reference Adaptive System for Torque Ripple Reduction in Sensorless Poly Phase Induction Motor Drive," Energies, MDPI, vol. 12(5), pages 1-25, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:5:p:920-:d:212489
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    References listed on IDEAS

    as
    1. Jing Tang & Yongheng Yang & Frede Blaabjerg & Jie Chen & Lijun Diao & Zhigang Liu, 2018. "Parameter Identification of Inverter-Fed Induction Motors: A Review," Energies, MDPI, vol. 11(9), pages 1-21, August.
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    Cited by:

    1. Usha Sengamalai & T. M. Thamizh Thentral & Palanisamy Ramasamy & Mohit Bajaj & Syed Sabir Hussain Bukhari & Ehab E. Elattar & Ahmed Althobaiti & Salah Kamel, 2022. "Mitigation of Circulating Bearing Current in Induction Motor Drive Using Modified ANN Based MRAS for Traction Application," Mathematics, MDPI, vol. 10(8), pages 1-24, April.

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