A Deep Learning Network based Robust Fault Diagnosis Method for IGBT Open Circuit

Yongjie Liu, Ariya Sangwongwanich, Yi Zhang, Rui Kong, Yingzhou Peng, Khalifa Al Hosan, Huai Wang

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Abstract

This paper proposes an IGBT open-circuit fault diagnosis method that can maintain high accuracy under diverse operation conditions and circuit parameter variances. Different aspects of uncertainties are analyzed in the component parameters, operation conditions, and measurement errors of a three-phase inverter case study. A lightweight Convolutional Neural Network (CNN) is applied based on an obtained dataset covering a wide range of inverter operation scenarios and uncertainties. The comparisons with benchmarked conventional fault diagnosis method and with different machine learning methods are presented. The results verify the improved accuracy in open-circuit diagnosis considering complex operation conditions and meanwhile with reduced detection time in certain scenarios.
OriginalsprogEngelsk
Titel2024 IEEE 10th International Power Electronics and Motion Control Conference, IPEMC 2024 ECCE Asia
Antal sider6
Vol/bindpp. 2366-2371
Udgivelsessted2024 IEEE 10th International Power Electronics and Motion Control Conference (IPEMC2024-ECCE Asia)
Publikationsdato2 jul. 2024
Sider2366-2371
ISBN (Trykt)979-8-3503-5134-7
ISBN (Elektronisk)979-8-3503-5133-0
DOI
StatusUdgivet - 2 jul. 2024

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