• DocumentCode
    2108074
  • Title

    Fault diagnosis for power electronic inverters: A model-based approach

  • Author

    Alavi, Marjan ; Luo, Ming ; Wang, Danwei ; Zhang, DanHong

  • fYear
    2011
  • fDate
    5-8 Sept. 2011
  • Firstpage
    221
  • Lastpage
    228
  • Abstract
    Each power electronic converter contains several semiconductor switches which are subject to faults. In this paper, an online model-based diagnosis algorithm is applied to a dc-ac half-bridge (HB) inverter to isolate switch faults. The circuit is modelled with hybrid bond graph (HBG) and the residuals are generated. The fault signature matrix (FSM) is used to determine the minimum number of detectors for isolable faults. A mode detection algorithm is proposed to estimate switch states. Short circuit switch faults are detected and isolated with the proposed mode detection algorithm. Least square method is adopted to identify the component faults. Incipient and abrupt faults in the power electronic inverter in switches, detectors, or other components are isolable using this model based approach.
  • Keywords
    DC-AC power convertors; bridge circuits; fault diagnosis; graph theory; invertors; least squares approximations; power semiconductor switches; DC-AC half-bridge inverter; fault diagnosis; fault signature matrix; hybrid bond graph; least square method; mode detection algorithm; power electronic inverters; semiconductor switches; short circuit switch faults; Circuit faults; Detectors; Equations; Integrated circuit modeling; Inverters; Mathematical model; Switches; Bond graph; diagnosis; fault diagnosis; hybrid bond graph; inverter; isolation; modelling; switch faults;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Diagnostics for Electric Machines, Power Electronics & Drives (SDEMPED), 2011 IEEE International Symposium on
  • Conference_Location
    Bologna
  • Print_ISBN
    978-1-4244-9301-2
  • Electronic_ISBN
    978-1-4244-9302-9
  • Type

    conf

  • DOI
    10.1109/DEMPED.2011.6063627
  • Filename
    6063627