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Performance Investigation of Switched Reluctance Motor Driven by Quasi-Z-Source Integrated Multiport Converter with Different Switching Algorithms

Author

Listed:
  • Mahmoud A. Gaafar

    (Aswan Power Electronics Applications Research Center (APEARC), Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • Arwa Abdelmaksoud

    (Aswan Power Electronics Applications Research Center (APEARC), Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • Mohamed Orabi

    (Aswan Power Electronics Applications Research Center (APEARC), Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • Hao Chen

    (School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Mostafa Dardeer

    (Aswan Power Electronics Applications Research Center (APEARC), Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

Abstract

Switched reluctance machines (SRMs) have received increasing attention for their many potential uses, such as for wind power and electric vehicle (EV) drive systems. The Quasi-Z-source Integrated Multiport Converter (QZIMPC) was recently introduced to improve the reliability of the SRM driver through small capacitance values. It is not possible, however, to simultaneously energize and deenergize two SRM phases in QZIMPC. This phenomenon can significantly increase the commutation period which, in turn, degrades the performance of SRM; in addition, this causes high-voltage ripples on the converter’s capacitors. Two switching algorithms are introduced and applied in this paper, and their performance with SRM is investigated in terms of torque ripple and peak phase current. The algorithms are based on prioritizing the control command in the on-going and off-going phases to fulfill the required load torque, as well as to accelerate the commutation process where possible. This is achieved without the interference of high-level controllers, which include speed controllers and/or torque ripple minimization. Through the simulation results, a comparison between the two switching algorithms is presented to determine their potential to improve the SRM drive system’s performance.

Suggested Citation

  • Mahmoud A. Gaafar & Arwa Abdelmaksoud & Mohamed Orabi & Hao Chen & Mostafa Dardeer, 2021. "Performance Investigation of Switched Reluctance Motor Driven by Quasi-Z-Source Integrated Multiport Converter with Different Switching Algorithms," Sustainability, MDPI, vol. 13(17), pages 1-14, August.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:17:p:9517-:d:620761
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    References listed on IDEAS

    as
    1. Vitor Fernão Pires & Armando José Pires & Armando Cordeiro & Daniel Foito, 2020. "A Review of the Power Converter Interfaces for Switched Reluctance Machines," Energies, MDPI, vol. 13(13), pages 1-34, July.
    2. Yuanfeng Lan & Yassine Benomar & Kritika Deepak & Ahmet Aksoz & Mohamed El Baghdadi & Emine Bostanci & Omar Hegazy, 2021. "Switched Reluctance Motors and Drive Systems for Electric Vehicle Powertrains: State of the Art Analysis and Future Trends," Energies, MDPI, vol. 14(8), pages 1-29, April.
    3. Brenda Rojas-Delgado & Monica Alonso & Hortensia Amaris & Juan de Santiago, 2019. "Wave Power Output Smoothing through the Use of a High-Speed Kinetic Buffer," Energies, MDPI, vol. 12(11), pages 1-28, June.
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    Cited by:

    1. Xinming Xu & Yang Gu & Guangjun Liu, 2022. "Study on a Wheel Electric Drive System with SRD for Loader," Energies, MDPI, vol. 15(10), pages 1-16, May.
    2. Wenmei Hao & Jie Hao & Zhifu Wang & Yi Hao, 2022. "Decoupling Characteristics and Torque Analytical Model of Sharing-Suspension-Windings Bearingless Switched Reluctance Motor Considering Flux-Linkage Saturation," Sustainability, MDPI, vol. 14(24), pages 1-14, December.
    3. Zheng Li & Xiaopeng Wei & Jinsong Wang & Libo Liu & Shenhui Du & Xiaoqiang Guo & Hexu Sun, 2022. "Design of a Deflection Switched Reluctance Motor Control System Based on a Flexible Neural Network," Energies, MDPI, vol. 15(11), pages 1-16, June.

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