赵泽宇,杜明星.多数据驱动人工神经网络的 IGBT
结温在线估计方法[J].电子测量与仪器学报,2022,36(7):223-229 |
多数据驱动人工神经网络的 IGBT
结温在线估计方法 |
On line estimation of IGBT junction temperature based onmulti data driven artificial neural network |
|
DOI: |
中文关键词: IGBT 结温估计 封装退化 数据驱动 神经网络 |
英文关键词:IGBT junction temperature estimation package degradation data driven neural network |
基金项目:天津市技术创新引导专项(20YDTPJC00510)项目资助 |
|
|
摘要点击次数: 567 |
全文下载次数: 1072 |
中文摘要: |
传统结温估计方法因其无法根据 IGBT 模块健康状态实时调校,从而导致当模块发生封装退化后无法准确估计结温。
因此,为解决在实际工况中模块封装退化造成的结温估计误差问题,建立了一个基于多数据驱动的以人工神经网络为主体的
IGBT 结温在线估计模型。 首先,确定饱和压降作为温敏电参数并研究其构成,分析其与集电极电流,芯片结温和封装退化之间
的耦合关系。 随后,为解决封装退化造成的饱和压降温度特性变化问题,提出结合米勒电压温度特性的优势,配合饱和压降与
集电极电流驱动人工神经网络算法构建结温估计模型,并通过搭建实验平台提取数据,完成模型的训练。 最终,通过与传统结
温估计方法对比估计误差,新模型将结温估计误差从 20%降低到了 5%以下。 |
英文摘要: |
Traditional junction temperature estimation methods cannot be adjusted according to the health status of IGBT module in real
time, which leads to inaccurate junction temperature estimation when the module is degraded. Therefore, to solve the problem of junction
temperature estimation error caused by module package degradation in actual conditions, this paper established a multi-data-driven IGBT
junction temperature online estimation model with artificial neural network as main body. Firstly, the saturation voltage drop was
determined as a thermoelectric parameter and its composition was studied. The coupling relationship between the saturation voltage drop,
collector current, chip junction temperature and package degradation are analyzed. Then, to solve the problem of temperature
characteristic change of saturation voltage drop caused by package degradation, a junction temperature estimation model was constructed
by combining the advantages of Miller voltage temperature characteristic and the artificial neural network algorithm driven by saturation
voltage drop and collector current. And the data were extracted by building an experimental platform to complete the training of the
model. Finally, by comparing the estimation error with the traditional junction temperature estimation method, the new model reduces the
estimation error from 20% to about 5%. |
查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|