IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i7p1372-d221280.html
   My bibliography  Save this article

Three-Level NPC Inverter Incipient Fault Detection and Classification using Output Current Statistical Analysis

Author

Listed:
  • Mehdi Baghli

    (Laboratoire IRECOM, University Djillali Liabès, 22000 Sidi Bel Abbes, Algeria
    GeePs, UMR 8507, CNRS, CentraleSupélec, Univ. Paris Sud, Université Paris Saclay, Sorbonne Univ, 75006 Paris, France)

  • Claude Delpha

    (L2S, UMR 8506, CNRS, CentraleSupélec, Univ. Paris Sud, Université Paris Saclay, 91190 Saint-Aubin, France)

  • Demba Diallo

    (GeePs, UMR 8507, CNRS, CentraleSupélec, Univ. Paris Sud, Université Paris Saclay, Sorbonne Univ, 75006 Paris, France
    Shanghai Maritime University, Department of Electrical Automation, Shanghai 201306, China)

  • Abdelhamid Hallouche

    (Laboratoire IRECOM, University Djillali Liabès, 22000 Sidi Bel Abbes, Algeria)

  • David Mba

    (Faculty of Computer Engineering and Media, De Montfort University, Leicester LE1 9BH, UK)

  • Tianzhen Wang

    (Shanghai Maritime University, Department of Electrical Automation, Shanghai 201306, China)

Abstract

This paper deals with open switch Fault Detection and Diagnosis (FDD) in three-level Neutral Point Clamped (NPC) inverter for electrical drives. The approach is based on the already available phase current time series measurements for different operating conditions (motor speed, load, and environment noise). Both fault detection and classification are studied and the efficiency performances of the proposed selected features are shown. For the fault detection, we focus on the first four statistical moments and the extracted features and then the Cumulative Sum (CUSUM) algorithm as the feature analysis technique to improve the performances. For the classification study, we propose to couple the knowledge on the faulty system brought by the statistical moments and the Kullback-Leibler divergence particularly suitable for the detection of incipient changes. The Principal Component Analysis (PCA) is then used to perform the classification. A 2D framework is obtained, which allows the faults to be classified efficiently within the considered operating conditions for all the selected fault durations.

Suggested Citation

  • Mehdi Baghli & Claude Delpha & Demba Diallo & Abdelhamid Hallouche & David Mba & Tianzhen Wang, 2019. "Three-Level NPC Inverter Incipient Fault Detection and Classification using Output Current Statistical Analysis," Energies, MDPI, vol. 12(7), pages 1-20, April.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1372-:d:221280
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/7/1372/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/12/7/1372/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Liang, Jinping & Zhang, Ke & Al-Durra, Ahmed & Zhou, Daming, 2020. "A novel fault diagnostic method in power converters for wind power generation system," Applied Energy, Elsevier, vol. 266(C).
    2. Masoud Ahmadipour & Hashim Hizam & Mohammad Lutfi Othman & Mohd Amran Mohd Radzi & Nikta Chireh, 2019. "A Fast Fault Identification in a Grid-Connected Photovoltaic System Using Wavelet Multi-Resolution Singular Spectrum Entropy and Support Vector Machine," Energies, MDPI, vol. 12(13), pages 1-18, June.
    3. Phong B. Dao, 2021. "A CUSUM-Based Approach for Condition Monitoring and Fault Diagnosis of Wind Turbines," Energies, MDPI, vol. 14(11), pages 1-19, June.
    4. Tito G. Amaral & Vitor Fernão Pires & Armando Cordeiro & Daniel Foito & João F. Martins & Julia Yamnenko & Tetyana Tereschenko & Liudmyla Laikova & Ihor Fedin, 2023. "Incipient Fault Diagnosis of a Grid-Connected T-Type Multilevel Inverter Using Multilayer Perceptron and Walsh Transform," Energies, MDPI, vol. 16(6), pages 1-18, March.

    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:12:y:2019:i:7:p:1372-:d:221280. 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.