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Mitigation of Circulating Bearing Current in Induction Motor Drive Using Modified ANN Based MRAS for Traction Application

Author

Listed:
  • Usha Sengamalai

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

  • T. M. Thamizh Thentral

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

  • Palanisamy Ramasamy

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

  • Mohit Bajaj

    (Department of Electrical and Electronics Engineering, National Institute of Technology Delhi, Delhi 110040, India)

  • Syed Sabir Hussain Bukhari

    (School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06910, Korea)

  • Ehab E. Elattar

    (Department of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Ahmed Althobaiti

    (Department of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Salah Kamel

    (Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

Abstract

Induction motors are popularly used in various applications because of the proposed modest construction, substantiated process, and limited size of specific power. The traditional AC traction drives are experimentally analyzed. There is a high circulating current due to the high Common-Mode Voltage (CMV). The high Circulating Bearing Current (CBC) is a major problem in conventional two-level voltage source inverter fed parallel-connected sensor-based induction motors for traction applications. A sensorless method is well known for shrinking costs and enhancing the reliability of an induction motor drive. The modified artificial neural network-based model reference adaptive system is designed to realize speed estimation methods for the sensorless drive. Four dissimilar multilevel inverter network topologies are being implemented to reduce CBC in the proposed sensorless traction motor drives. The multilevel inverter types are T-bridge, Neutral Point Clamped Inverter (NPC), cascaded H-bridge, and modified reduced switch topologies. The four methods are compared, and the best method has been identified in terms of 80% less CMV compared to the conventional one. The modified cascaded H-bridge inverter reduces the CBC of the proposed artificial neural network-based parallel connected induction motor; it is 50% compared to the conventional method. The CBC of the modified method is analyzed and associated with the traditional method. Finally, the parallel-connected induction motor traction drive hardware is implemented, and the performance is analyzed.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:8:p:1220-:d:789415
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    References listed on IDEAS

    as
    1. Michal Adamczyk & Teresa Orlowska-Kowalska, 2021. "Self-Correcting Virtual Current Sensor Based on the Modified Luenberger Observer for Fault-Tolerant Induction Motor Drive," Energies, MDPI, vol. 14(20), pages 1-16, October.
    2. Bowei Zou & Yougui Guo & Xi Xiao & Bowen Yang & Xiao Wang & Mingzhang Shi & Yulin Tu, 2020. "Performance Improvement of Matrix Converter Direct Torque Control System," Energies, MDPI, vol. 13(12), pages 1-17, June.
    3. Tadeusz Białoń & Marian Pasko & Roman Niestrój, 2020. "Developing Induction Motor State Observers with Increased Robustness," Energies, MDPI, vol. 13(20), pages 1-24, October.
    4. Amr. S. Zalhaf & Mazen Abdel-Salam & Mahmoud Ahmed, 2019. "An Active Common-Mode Voltage Canceler for PWM Converters in Wind-Turbine Doubly-Fed Induction Generators," Energies, MDPI, vol. 12(4), pages 1-12, February.
    5. 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.
    6. Ruifang Liu & Xin Ma & Xuejiao Ren & Junci Cao & Shuangxia Niu, 2018. "Comparative Analysis of Bearing Current in Wind Turbine Generators," Energies, MDPI, vol. 11(5), pages 1-13, May.
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    Cited by:

    1. Abdelhak Boudallaa & Ahmed Belkhadir & Mohammed Chennani & Driss Belkhayat & Youssef Zidani & Karim Rhofir, 2023. "Real-Time Implementation of Sensorless DTC-SVM Applied to 4WDEV Using the MRAS Estimator," Energies, MDPI, vol. 16(20), pages 1-23, October.
    2. Shaoping Wang & Jun Zhou & Zhaoxia Duan, 2023. "Finite Frequency H ∞ Control for Doubly Fed Induction Generators with Input Delay and Gain Disturbance," Sustainability, MDPI, vol. 15(5), pages 1-19, March.
    3. Sebastian Berhausen & Tomasz Jarek & Petr Orság, 2022. "Influence of the Shielding Winding on the Bearing Voltage in a Permanent Magnet Synchronous Machine," Energies, MDPI, vol. 15(21), pages 1-19, October.

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