Multiple open switch fault diagnosis of three phase voltage source inverter using ensemble bagged tree machine learning technique

Chukwuemeka Ibem*, M E A Farrag, Ahmed Aboushady, Sherif Dabour

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
101 Downloads (Pure)

Abstract

Three-phase converters based on insulated-gate bipolar transistors (IGBTs) are widely used in various industrial applications. Faults in IGBTs can significantly affect the operation and safety of the power electronic equipment and loads. It is critical to accurately detect power inverter faults as soon as they occur to ensure system availability and high-power quality. This study provides a novel integration of signal and data-driven fault-diagnosis approaches for detecting open-circuit switch faults in three-phase inverters. The proposed technique uses the average root-mean-square (RMS) ratio of the phase current as the key extraction feature. This feature can be used to estimate the fault types and faulty switches (es) irrespective of changes
in the running load. Ensemble-bagged machine learning classification was used to accurately predict the faulty switch of the inverter. The results demonstrate the ability of the proposed fault diagnosis technique to identify single-, double-, and triple-switch fault (s). The experimental results also attested to the simulation of multiple fault diagnosis. A unique feature of this technique is its ability to estimate faulty switches under various inverter-operating conditions.
Original languageEnglish
Pages (from-to)85865-85877
Number of pages13
JournalIEEE Access
Volume11
Early online date10 Aug 2023
DOIs
Publication statusPublished - 2023

Keywords

  • Ensemble bagged, Fault diagnosis, Open-circuit fault, Voltage source inverter, IGBT
  • fault diagnosis
  • IGBT
  • voltage source inverter
  • Ensemble bagged
  • open-circuit fault

ASJC Scopus subject areas

  • General Engineering
  • General Materials Science
  • General Computer Science

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  • New fuzzy logic based switch-fault diagnosis in three phase inverters

    Ibem, C. N., Farrag, M. E. & Aboushady, A. A., 30 Sept 2020, 2020 55th International Universities Power Engineering Conference (UPEC). IEEE, 6 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Open Access
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    5 Citations (Scopus)
    268 Downloads (Pure)
  • Enhanced fault diagnosis of DFIG converter systems

    Ibem, C. N., Farrag, M. E. & Aboushady, A. A., 7 Nov 2019, 2019 54th International Universities Power Engineering Conference (UPEC). IEEE, p. 1-6 6 p. (2019 54th International Universities Power Engineering Conference, UPEC 2019 - Proceedings).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Open Access
    File
    3 Citations (Scopus)
    188 Downloads (Pure)

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