Intelligent Condition Monitoring of Multiple Thermal Degradation of IGBT Modules Based on Case Temperature Matrix

被引:2
|
作者
Zhan, Cao [1 ]
Tang, Yizheng [2 ]
Zhu, Lingyu [2 ]
Wang, Weicheng [2 ]
Gou, Yating [2 ]
Ji, Shengchang [2 ]
Iannuzzo, Francesco [3 ]
机构
[1] Virginia Tech, Ctr Power Elect Syst CPES, Bradley Dept Elect & Comp Engn ECE, Blacksburg, VA 24061 USA
[2] Xi An Jiao Tong Univ, State Key Lab Elect Insulat & Power Equipment, Xian 710049, Peoples R China
[3] Aalborg Univ, Dept Energy Technol, Aalborg, Denmark
关键词
Insulated gate bipolar transistors; Degradation; Thermal degradation; Temperature measurement; Temperature sensors; Thermal resistance; Monitoring; Case temperature matrix; condition monitoring; degradation type and level; insulated gate bipolar transistor (IGBT) module; JUNCTION TEMPERATURE; POWER MODULES; FATIGUE;
D O I
10.1109/TPEL.2024.3415439
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This article presents an intelligent approach for monitoring various thermal degradations in insulated gate bipolar transistor (IGBT) modules. Thermal degradation at the baseplate solder and thermal interface material (TIM) both induce changes in the case temperature distribution, offering insights into the corresponding degradation type and level. To capture this information, a strategically placed thermocouple array forms a temperature matrix on the case surface. The normalization of the case temperature matrix effectively mitigates the influence of working conditions, such as load current or heatsink temperature. Furthermore, a model augmented with a convolutional neural network is utilized to classify the degradation type and level based on temperature matrixes. To generate datasets under diverse operational conditions, a finite-element model of a conventional IGBT module is developed. The model includes various degradation locations (baseplate solder fatigue, TIM degradation, baseplate solder, and TIM degradation) and levels (health, early degradation, severe degradation, failure). Gaussian noise is introduced to the simulation data to enhance both training and prediction accuracy. Additionally, experimental data from a two-level inverter is incorporated into the training dataset to further refine accuracy. The results affirm that this approach excels in accurately distinguishing the degradation type and evaluating the degradation level.
引用
收藏
页码:12490 / 12501
页数:12
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