IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i24p8203-d696595.html
   My bibliography  Save this article

Analysis Performance of SRM Based on the Novel Dependent Torque Control Method

Author

Listed:
  • Piotr Bogusz

    (The Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, 35-959 Rzeszow, Poland)

  • Mariusz Korkosz

    (The Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, 35-959 Rzeszow, Poland)

  • Jan Prokop

    (The Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, 35-959 Rzeszow, Poland)

  • Mateusz Daraż

    (The Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, 35-959 Rzeszow, Poland)

Abstract

This paper presents a description and the results of simulations and laboratory tests of proposed methods for dependent torque control in a Switched Reluctance Motor (SRM). The proposed methods are based on Dependent Torque Motor Control (Rising Slope), DTMC (RC) , and Dependent Torque Motor Control (Falling Slope), DTMC (FC) . The results of these studies were compared with those on the Classical Torque Motor Control (CTMC) method. Studies were conducted for each of the analyzed control methods by determining the efficiency of the drive and the RMS of the source current and analyzing the vibrations generated for each of the control methods. The harmonics of the phase currents, which caused an increase in the level of vibrations generated, were determined. The usefulness of the proposed methods for controlling SRMs was assessed based on simulations and experiments. Additionally, the natural frequencies of the stator of the tested SRM were determined by a simulation using the Ansys Maxwell suite. The levels of vibration acceleration generated by the SRM were compared for the considered control methods.

Suggested Citation

  • Piotr Bogusz & Mariusz Korkosz & Jan Prokop & Mateusz Daraż, 2021. "Analysis Performance of SRM Based on the Novel Dependent Torque Control Method," Energies, MDPI, vol. 14(24), pages 1-18, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:24:p:8203-:d:696595
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/24/8203/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/24/8203/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:24:p:8203-:d:696595. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.