IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/9505845.html
   My bibliography  Save this article

PWM-VSI Diagnostic and Reconfiguration Method Based on Fuzzy Logic Approach for SSTPI-Fed IM Drives under IGBT OCFs

Author

Listed:
  • Mohamed Ali Zdiri
  • Mohsen Ben Ammar
  • Fatma Ben Salem
  • Hsan Hadj Abdallah

Abstract

Due to the importance of the drive system reliability, several diagnostic methods have been investigated for the SSTPI-IM association in the literature. Based on the normalized currents and the current vector slope, this paper investigates a fuzzy diagnostic method for this association. The fuzzy logic technique is appealed in order to process the diagnosis variable symptoms and the faulty IGBT information. Indeed, the design, inputs, and rules of the fuzzy logic are distinct compared with the other existing diagnostic methods. The proposed fuzzy diagnostic method allows the best efficient detection and identification of the single and phase OCF of the SSTPI-IM association. Accordingly, after the fault detection and identification using this proposed FLC diagnostic method, a reconfiguration step of IGBT OCFs must be applied in order to compensate for these faults and ensure the drive system continuity. This reconfiguration is based on the change of the SSTPI-IM topology to the FSTPI-IM topology by activating or deactivating the used relays. Several simulation results utilizing a direct RFOC controlled SSTPI-IM drive system are investigated, showing the fuzzy diagnostic and reconfiguration methods’ performances, their robustness, and their fast fault detection during distinct operating conditions.

Suggested Citation

  • Mohamed Ali Zdiri & Mohsen Ben Ammar & Fatma Ben Salem & Hsan Hadj Abdallah, 2021. "PWM-VSI Diagnostic and Reconfiguration Method Based on Fuzzy Logic Approach for SSTPI-Fed IM Drives under IGBT OCFs," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-18, July.
  • Handle: RePEc:hin:jnlmpe:9505845
    DOI: 10.1155/2021/9505845
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2021/9505845.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/MPE/2021/9505845.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2021/9505845?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

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


    Cited by:

    1. Mohamed Ali Zdiri & Tawfik Guesmi & Badr M. Alshammari & Khalid Alqunun & Abdulaziz Almalaq & Fatma Ben Salem & Hsan Hadj Abdallah & Ahmed Toumi, 2022. "Design and Analysis of Sliding-Mode Artificial Neural Network Control Strategy for Hybrid PV-Battery-Supercapacitor System," Energies, MDPI, vol. 15(11), pages 1-20, June.

    More about this item

    Statistics

    Access and download statistics

    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:hin:jnlmpe:9505845. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.