基于深度学习的压接式IGBT运行特性预测方法

肖凯; 王振; 邹延生; 严喜林; 许琳浩; 黄晨浩

doi:10.13296/j.1001-1609.hva.2026.10.026

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基于深度学习的压接式IGBT运行特性预测方法

技术讨论 | 更新时间：2026-10-14

- 基于深度学习的压接式IGBT运行特性预测方法
- Operation Characteristics Prediction Method of Press-pack IGBT Based on Deep Learning
- 高压电器 2026年62卷第10期页码：227-236
- 作者机构：
  
  中国南方电网有限责任公司超高压输电公司电力科研院，广州 510663
  湖南大学电气与信息工程学院，长沙 410082
- 作者简介：
  
  肖凯（1991—），男，工程师，从事高压直流输电换流阀及阀冷工作（通信作者）（E-mail：xiaokaiehv@163.com）。
- 基金信息：
  
  中国南方电网有限责任公司科技项目Science and Technology Project of China Southern Power Grid.(CGYKJXM20220108)
- DOI：10.13296/j.1001-1609.hva.2026.10.026
  中图分类号：
- 收稿：2026-05-22，
  
  修回：2026-07-10，
  
  纸质出版：2026-10-16
- 稿件说明：
移动端阅览
肖凯, 王振, 邹延生, 等. 基于深度学习的压接式IGBT运行特性预测方法[J]. 高压电器, 2026,62(10):227-236.

XIAO Kai, WANG Zhen, ZOU Yansheng, et al. Operation Characteristics Prediction Method of Press-pack IGBT Based on Deep Learning[J]. High Voltage Apparatus, 2026, 62(10): 227-236.
肖凯, 王振, 邹延生, 等. 基于深度学习的压接式IGBT运行特性预测方法[J]. 高压电器, 2026,62(10):227-236. DOI： 10.13296/j.1001-1609.hva.2026.10.026.

XIAO Kai, WANG Zhen, ZOU Yansheng, et al. Operation Characteristics Prediction Method of Press-pack IGBT Based on Deep Learning[J]. High Voltage Apparatus, 2026, 62(10): 227-236. DOI： 10.13296/j.1001-1609.hva.2026.10.026.

摘要

压接式IGBT是柔直输电系统的关键环境，其寿命评估是提高系统可靠性的重要环节，其中掌握IGBT模块多场特征是关键。尽管现有多场仿真技术能够实现压接式IGBT模块的完备分析，但是存在计算效率低的问题。因此，文中提出了融合深度学习和多场模型的压接式IGBT模块多场运行特征分析方法，以实现压接IGBT模块运行信息的快速获取，支撑其数字孪生模型的构建。首先，构建了压接式IGBT模块电—热—力多场耦合模型，分析了不同工况下IGBT模型重要寿命评估特征变化规律；其次，提出了BPBR神经网络与网格搜索法相结合的深度学习方法，以实现压接式IGBT模块多场耦合信息的快速预测；最后，测试了所提出方法的高效性，结果表明：所提出模型能够有效分析压接式IGBT模块多场信息，与有限元计算结果吻合度好，偏差率小于1%，且计算时间不到仿真的万分之一。研究成果能够支撑压接式IGBT模块多场信息的快速获取，进而支撑压接式IGBT模块数字孪生体的构建和状态实时监测。

Abstract

Press-pack IGBT is the key environment of VSC-HVDC transmission system

and its life assessment is a crucial part of improving system reliability

in which mastering the multi-field characteristics of IGBT module is of critical importance. Although the existing multi-field simulation technology can achieve thorough analysis of IGBT module

its computational efficiency remains limited. Therefore

in this paper an analysis method of multi-field operation characteristics of IGBT module with deep learning and multi-field model is proposed in order to realize the rapid acquisition of operation information of IGBT module and support the construction of its digital twin model. First

the electric-thermal-mechanical multi-field coupling model of press-pack IGBT module is constructed

and the variation of important life assessment characteristics of IGBT model under different working conditions is analyzed. Then

a deep learning method combining BPBR neural network and grid search method is proposed to realize the rapid prediction of multi-field coupling information of IGBT module. Finally

the high efficiency of the proposed method is tested. The result shows that the proposed model can effectively analyze the multi-field information of the IGBT module

which is in good agreement with the finite element calculation results

with deviation rate less than 1% and calculation time less than one ten-thousandth of that of the simulation. The research results can support the rapid acquisition of multi-field information of IGBT module

and then support the construction and real-time monitoring of digital twins of IGBT module.

关键词

Keywords

references

郑全旭,孙小平,娄彦涛,等.±500 kV海上柔直换流阀轻型化设计及仿真分析[J].高压电器,2023,59(6):188-201.

Zheng Quanxu,Sun Xiaoping,Lou Yantao,et al.Lightweight design and simulation analysis of ±500 kV offshore VSC converter valve[J].High Voltage Apparatus,2023,59(6):188-201.

贾科,刘可歆,毕天姝,等.基于波形匹配的柔性直流输电线路单端量保护研究[J].电力系统保护与控制,2025,53(3):13-26.

Jia Ke,Liu Kexin,Bi Tianshu,et al.Research on single-ended protection of a flexible HVDC transmission line based on waveform matching[J].Power System Protection and Control ,2025,53(3):13-26.

孙凯,卢世蕾,易哲嫄,等.面向电力电子变压器应用的大容量高频变压器技术综述[J].中国电机工程学报,2021,41(24):8531-8545.

Sun Kai,Lu Shilei,Yi Zheyuan,et al.A review of high-power highfrequency transformer technology for power electronic transformer applications[J].Proceedings of the CSEE,2021,41(24):8531-8545.

Han Lubin,Liang Lin,Wang Yijian,et al. Performance limits of high voltage press-pack SiC IGBT and SiC MOSFET devices[J].Power Electronic Devices and Components,2022(3):100019.

Oh H,Han B,Mccluskey P,et al.Physics-of-failure,condition monitoring,and prognostics of insulated gate bipolar transistor modules:a review[J].IEEE Transactions on Power Electronics, 2015,30(5):2413-2426.

Huang Zhiliang,Yang Tiangui,Li Chuanbo,et al.Optimization of the pressure distribution in press-pack insulated gate bipolar transistors[J].Structural and Multidisciplinary Optimization,2021, 63(2):855-865.

杨兵,王胜利,王文杰,等.混合式直流断路器IGBT关断能力提升技术研究[J].高压电器,2020,56(5):24-32.

Yang Bing,Wang Shengli,Wang Wenjie,et al.Research on IGBT turning off capacity enhancement technology of hybrid DC circuit breaker[J].High Voltage Apparatus,2020,56(5):24-32.

Liu Renkuan,Li Hui,Yao Ran,et al.Research on long-term reliability of silver sintered press-pack IGBT modules[J].Power Electronic Devices and Components,2022 (3):100012.

Lutz J,Schlangenotto H,Scheyermann U,et al.Semiconductor power devices:physics,characteristics,reliability[M].Berlin Heidelberg:Springer-Verlag,2011.

任斌,黄永章.压接型IGBT器件单芯片子模组可靠性寿命仿真研究[J].华北电力大学学报(自然科学版),2020,47(3):49-57.

Ren Bin,Huang Yongzhang.Simulation on reliability lifetime of single IGBT chip module of press-pack IGBTs[J].Journal of North China Electric Power University(Natural Science Edition),2020,47(3):49-57.

张经纬.基于有限元法的压接型IGBT器件单芯片子模组疲劳寿命预测[D].北京:华北电力大学,2018.

Zhang Jingwei.Fatigue life prediction of singl-echip submodule of press-pack IGBTs based on finite element method[D].Beijing:North China Electric Power University,2018.

郭佳奇,张一鸣,邓二平,等.考虑接触界面全接触的压接型IGBT器件功率循环寿命预测[J].华北电力大学学报(自然科学版),2023,50(2):63-72.

Guo Jiaqi,Zhang Yiming,Deng Erping,et al.Prediction of power cycle life of press-pack IGBTs considering full contact of contact interface[J].Journal of North China Electric Power University (Natural Science Edition),2023,50(2):63-72.

任斌.压接型IGBT器件可靠性寿命模型与相关仿真研究[D].北京:华北电力大学,2020.

Ren Bin.Simulation on reliability lifetime of single IGBT chip module of press-pack IGBTs[D].Beijing:North China Electric Power University,2020.

韩文超,路茂增,马新喜,等.高压大容量混合型MMC半桥子模块下部IGBT损耗优化方法[J].电力工程技术,2024,43(2):189-198.

Han Wenchao,Lu Maozeng,Ma Xinxi,et al.Loss optimization method for bottom IGBT in half bridge sub-module of high voltage and large capacity hybridMMC[J].Electric Power Engineering Technology,2024,43(2):189-198.

邓吉利.柔性直流换流阀压接式IGBT器件可靠性建模与评估[D].重庆:重庆大学,2018.

Deng Jili.Reliability modeling and evaluation of press-pack IGBT devices forVSC-HVDCconverter valves[D].Chongqing:Chongqing University,2018.

邓真宇,陈民铀,赖伟,等.多芯片并联压接式IGBT中温度不均对电流分布的影响[J].中国电力,2020,53(12):10-17.

Deng Zhenyu,Chen Minyou,Lai Wei,et al.Influence of uneven temperature on current distribution in paralleled multi-chips press packIGBT[J].Electric Power,2020,53(12):10-17.

李洁,党晓圆,邓真宇,等.多芯片并联压接式IGBT器件多物理场耦合建模分析[J].电力电子技术,2021,55(12):9-12.

Li Jie,Dang Xiaoyuan,Deng Zhenyu,et al.Multi-physical field coupling modeling analysis of multi-chip parallel press pack IGBT devices[J].Power Electronics,2021,55(12):9-12.

张筱迪,毛明晖,卢昶衡,等.基于有限元分析和机器学习的跌落所致封装结构力学行为预测[J].电子与封装,2021,21 (2):73-80.

Zhang Xiaodi,Mao Minghui,Lu Changheng,et al.Prediction of mechanical behavior of package structure subjected to drop impact based on finite element analysis and machine learning[J].Electronics and Packaging,2021,21(2):73-80.

金亮,王飞,杨庆新,等.永磁同步电机性能分析的典型深度学习模型与训练方法[J].电工技术学报,2018,33(s1):41-48.

Jin Liang,Wang Fei,Yang Qingxin,et al.Typical deep learning model and training method for performance analysis of permanent magnet synchronous motor[J].Transactions of China Electrotechnical Society,2018,33(s1):41-48.

王要强,陈晨,张世达,等.基于新型趋近律的表贴式永磁同步电机抗扰滑模控制策略[J].电力系统保护与控制,2026,54(2):151-161.

Wang Yaoqiang,Chen Chen,Zhang Shida,et al.Anti-disturbance sliding mode control strategy for surface - mounted permanent magnet synchronous motors based on a novel reaching law[J].Power System Protection and Control,2026,54(2):151-161.

魏尧,付俊荣,王高林,等.基于陷波抗停滞的永磁同步电机无模型预测电流滑模控制[J].电工技术学报,2026,41(2):475-486.

Wei Yao,Fu Junrong,Wang Gaolin,et al.Notch anti-stagnation based model - free predictive current sliding mode control of permanent magnet synchronous motor drives[J].Transactions of China Electrotechnical Society,2026,41(2):475-486.

李耀华,种国臣,王钦政,等.永磁同步电机模型预测电流控制简化研究[J].电力电子技术,2026,60(2):137-143.

Li Yaohua,Chong Guochen,Wang Qinzheng,et al.Research on simplified model predictive current control for permanent magnet synchronous motor[J].Power Electronics,2026,60(2):137-143.

宋诗雨,吴永斌,尹海韬,等.基于混合式BPNN/PSO-MRAS的永磁同步电机参数在线辨识[J].智慧电力,2026,54(4):111-118.

Song Shiyu,Wu Yongbin,Yin Haitao,et al.Online parameter identification of permanent magnet synchronous motor based on hybridBPNN/PSO-MRAS[J].Smart Power,2026,54(4):111-118.

王鸣宇,汤毅,许彦祺,等.表贴式永磁同步电机空载电磁性能解析分析[J].大电机技术,2026(1):40-48.

Wang Mingyu,Tang Yi,Xu Yanqi,et al. Analytical analysis of noload electromagnetic performance of surface mounted permanent magnet synchronous motor[J].Large Electric Machine and Hydraulic Turbine,2026(1):40-48.

刘向贞.永磁同步电机性能分析的深度学习算法与应用[D].天津:天津工业大学,2020.

Liu Xiangzhen.Deep learning algorithm for performance analysis of permanent magnet synchronous motor and its application[D].Tianjin:Tiangong University,2020.

阎世梁,王银玲,路丹丹,等.基于深度神经网络的永磁直线电机仿真与优化[J].系统仿真学报,2024,36(3):713-725.

Yan Shiliang,Wang Yinling,Lu Dandan,et al.Simulation and optimization of permanent magnet linear machine based on deep neural network[J].Journal of System Simulation ,2024 ,36(3) :713-725.

Jing Honglei. Deep neural network based chiller energy saving control method[J].Computer Informatization and Mechanical System,2023,6(5):73-78.

El-dalahmeh M ,Al-greer M ,El-dalahmeh M ,et al. Capacity estimation of lithium-ion batteries based on adaptive empirical wavelet transform and long short-term memory neural network[J].Journal of Energy Storage,2023 (70):108046.

Zhao Meiling,Zhang Yunwei,Yuan Zhanbin,et al.A point-to-point convolutional neural network for reconstructing electromagnetic parameters of multiple cavities scattering with inhomogeneous anisotropic media[J].Engineering Analysis With Boundary Elements,2023(155):281-296.

耿学锋,何赟泽,李孟川,等.IGBT多物理场建模技术与应用研究概述[J].中国电机工程学报,2022,42(1):271-290.

Geng Xuefeng,He Yunze,Li Mengchuan,et al.An overview of IGBT multiphysics modeling technology and application[J].Proceedings of the CSEE,2022,42(1):271-290.

范继锋,王永强,张天龙,等.基于时频分析矩阵与BP神经网络的输电线路保护方法[J].电力科学与技术学报,2026,41(1):46-52.

Fan Jifeng,Wang Yongqiang,Zhang Tianlong,et al.Transmission line protection method based on time-frequency analysismatrix and BP neural network[J].Journal of Electric Power Science and Technology,2026,41(1):46-52.

夏世远,苏建徽,杜燕,等.基于贝叶斯正则化BP神经网络的PEMFC电堆建模[J].合肥工业大学学报(自然科学版),2021,44 (7):895-899.

Xia Shiyuan,Su Jianhui,Du Yan,et al. Modeling of PEMFC stack based on Bayesian regularized BP neural network[J].Journal of Hefei University of Technology(Natural Science),2021,44(7):895-899.

Ghahramani Z.Probabilistic machine learning and artificial intelligence[J].Nature,2015,521(7553):452-459.

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