Статья

A Junction Temperature Monitoring Method for IGBT Modules Based on Turn-Off Voltage With Convolutional Neural Networks

Huimin WangMinistry of Education Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Southwest Jiaotong University, Chengdu, ChinaZhiliang XuMinistry of Education Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Southwest Jiaotong University, Chengdu, ChinaXinglai GeMinistry of Education Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Southwest Jiaotong University, Chengdu, ChinaYongkang LiaoMinistry of Education Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Southwest Jiaotong University, Chengdu, ChinaYongheng YangCollege of Electrical Engineering, Zhejiang University, Hangzhou, ChinaYi ZhangDepartment of Energy Technology, Aalborg University, Aalborg, DenmarkBo YaoDepartment of Energy Technology, Aalborg University, Aalborg, DenmarkYuheng ChaiMinistry of Education Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Southwest Jiaotong University, Chengdu, China

2023en

ABI

Аннотация

Junction temperature monitoring (JTM) is essential for reliability evaluation and health management for insulated-gate bipolar transistor (IGBT) modules, and thus is extensively focused on in power electronics converters. However, many JTM methods for IGBT modules are criticized for providing inaccurate junction temperature information with strong load current dependence. To address this, a JTM method based on the turn- <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> voltage (TOV) and convolutional neural networks (CNN) is proposed in this article. In this method, the TOV is used as the junction temperature indicator, and the characterization behavior of the TOV during turn- <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> transient process is thoroughly analyzed. Then, the parameter dependence of the TOV is investigated. Considering the proposed JTM method may be subject to the issue of load current dependence and show an undesirable performance, the CNN is adopted to maintain the accuracy of junction temperature prediction due to its excellent global and local feature recognition capability. With this regards, the proposed JTM method enables to provide accurate junction temperature information under different conditions. Finally, the double-pulse tests and experimental tests are carried out to validate the effectiveness of the proposed JTM method.

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Идентификаторы

DOI: 10.1109/tpel.2023.3278675

Цитирования и источники

Цитирований: 2Использованных источников: 0

Показатели — AkademScholar