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    Fault Recognition of Multilevel Inverter Using Artificial Neural Network Approach

    Aravind Athimoolam1,*, Karthik Balasubramanian2

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1331-1347, 2023, DOI:10.32604/iasc.2023.033465

    Abstract This paper focuses on the development of a diagnostic tool for detecting insulated gate bipolar transistor power electronic switch flaws caused by both open and short circuit faults in multi-level inverter time-frequency output voltage specifications. High-resolution laboratory virtual instrument engineering workbench software testing tool with a sample rate data collection system, as well as specialized signal processing and soft computing technologies, are used in this proposed method. On a single-phase cascaded H-bridge multilevel inverter, simulation and experimental investigations of both open and short issues of the insulated gate bipolar transistor components are performed out. In all conceivable switch issues, the… More >

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