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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
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    Citations

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    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.

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