马汉卿
MA Hanqing

• 1:西安工程大学电子信息学院

摘要(Abstract)：

IGBT(绝缘栅双极型晶体管)在电力系统、交通、军工、航天等诸多领域得到广泛应用,其可靠性关乎整个系统的稳定,而模块的结温是IGBT可靠性研究中至关重要的一环。但在实际工作电路中,其芯片封装在IGBT内部,要实现结温的实时检测非常困难。在此条件下,综合考虑各结温测量方法的可行性及测量的准确性后,选取饱和压降作为热敏感电参数来预测结温。利用饱和压降测试平台提取所需热敏感电参数,然后结合试验数据建立了基于误差反向传播算法的IGBT模块结温预测神经网络模型,构建热敏感电参数与结温的映射关系。避开IGBT模块的物理结构,实现了对IGBT在实际工况条件下结温的准确预测。
IGBT(Insulated Gate Bipolar Transistor) is widely used in power system, transportation, military,aerospace and many other fields. Its reliability is related to the stability of the whole system, and the junction temperature of the module is crucial in IGBT reliability research. However, in the actual working circuit, the chip is packaged inside the IGBT, and real-time detection of the junction temperature is very difficult. Under these conditions, considering the feasibility of the implementation of each junction temperature measurement method and the accuracy of the measurement, the saturation pressure drop is selected as the thermal parameter to predict the junction temperature. The saturation voltage drop test platform is used to extract the required thermal sensitive parameters, and then the back propagation(BP) based IGBT module junction temperature prediction neural network model is established based on the experimental data. The thermal sensitive electrical parameters and junction temperature are constructed to map the relationship. The physical structure of the IGBT module is avoided, and the accurate prediction of the junction temperature of the IGBT under actual working conditions is realized.

关键词(KeyWords)： IGBT;结温检测;神经网络;热敏感电参数;饱和压降
IGBT;junction temperature detection;neural network;thermal sensitive electrical parameters;saturation voltage drop

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金资助项目(51477138)

作者(Author): 马汉卿
MA Hanqing

DOI: 10.19585/j.zjdl.201904003

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