IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i18p5004-d1753950.html
   My bibliography  Save this article

Open-Circuit Fault Detection in a 5-Level Cascaded H-Bridge Inverter Using 1D CNN and LSTM

Author

Listed:
  • Chouaib Djaghloul

    (Department of Scientific Instrumentation, Group of Physics of Materials (GPM), UMR 6634, CNRS, INSA Rouen Normandie, University of Rouen Normandie, Normandie University, 76000 Rouen, France
    Department of Electrical Engineering, Institut Pascal, CNRS, Clermont Auvergne INP, University of Clermont Auvergne, 63000 Clermont-Ferrand, France)

  • Kambiz Tehrani

    (Department of Electrical Engineering, Institut Pascal, CNRS, Clermont Auvergne INP, University of Clermont Auvergne, 63000 Clermont-Ferrand, France)

  • François Vurpillot

    (Department of Scientific Instrumentation, Group of Physics of Materials (GPM), UMR 6634, CNRS, INSA Rouen Normandie, University of Rouen Normandie, Normandie University, 76000 Rouen, France)

Abstract

It is well known that power converters have the highest failure rate in the energy conversion chain in different industrial applications. This could definitely affect the reliability of the system. The reliability of converters in power conversion systems is crucial, as failures can lead to critical consequences and damage other system components. Therefore, it is important to predict and detect failures and take corrective actions to prevent them. One of the most common types of failure in power converters is semiconductor failure, which can manifest as an open circuit or a short circuit. This paper focuses on single and double open-circuit switch failures in a 5-level cascaded H-bridge inverter, for which a fast, precise method is required. A data-driven approach is employed here, using the output voltage and voltages across each H-bridge as diagnostic signals. A 1D-CNN LSTM neural network is trained to accurately detect and localize open-circuit faults, providing a reliable, practical solution.

Suggested Citation

  • Chouaib Djaghloul & Kambiz Tehrani & François Vurpillot, 2025. "Open-Circuit Fault Detection in a 5-Level Cascaded H-Bridge Inverter Using 1D CNN and LSTM," Energies, MDPI, vol. 18(18), pages 1-20, September.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:18:p:5004-:d:1753950
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/18/5004/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/18/5004/
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    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:gam:jeners:v:18:y:2025:i:18:p:5004-:d:1753950. 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.