4.8 Article

Active Common-Mode Voltage-Based Open-Switch Fault Diagnosis of Inverters in IM-Drive Systems

Journal

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Volume 68, Issue 1, Pages 103-115

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2020.2965430

Keywords

Active common-mode voltage injection (ACMVI); calculated common-mode voltages (CCMVs); fault diagnosis; induction motor (IM); open-switch faults; voltage-source inverter (VSI)

Funding

  1. National Key R&D Program of China [2018YFB0606005]
  2. National Natural Science Foundation of China [61622311]
  3. Project of Innovation-Driven Plan in Central South University [2019CX003, 2018XK2002]

Ask authors/readers for more resources

A robust open-switch fault-diagnosis method based on the common-mode voltages of the inverter in IM-drive systems is proposed in this study, utilizing the characteristics of common-mode voltages for fault detection and localization. An active common-mode voltage injection method is introduced to improve diagnosis results credibility and reduce missed detection rate. The method only uses measured current information, avoiding the application of extra sensors.
Fast and reliable fault diagnosis is essential for induction motor (IM) drive systems with high-reliability requirements. In this article, we propose a robust open-switch fault-diagnosis method based on the common-mode voltages of the inverter in the IM-drive systems. This method makes full use of the characteristics of common-mode voltages. First, the calculated common-mode voltages (CCMVs) are equal to each other under normal operating conditions and the CCMVs represent the other behaviors under fault conditions. Second, average common-mode voltage is a degree of freedom in the modulation of the inverter. Based on the first characteristics, the CCMVs are used to detect and locate the open-switch faults of the inverters. Based on the second one, an active common-mode voltage injection method is proposed to improve the credibility of the diagnosis results and reduce the rate of missed detection. The proposed diagnosis algorithm only uses the measured current information, avoiding the application of extra sensors. And its average detection time is about 300 mu s, which is three switching cycles. Simulation and experimental results are presented to demonstrate the feasibility and effectiveness of the proposed fault-diagnosis method.

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