← 返回
基于卷积神经网络的功率器件结温监测
Junction Temperature Monitoring of Power Devices Using Convolutional Neural Networks
| 作者 | Zhiliang Xu · Huimin Wang · Xinglai Ge · Yichi Zhang · Dong Xie · Bo Yao |
| 期刊 | IEEE Transactions on Industry Applications |
| 出版日期 | 2025年3月 |
| 技术分类 | 电动汽车驱动 |
| 技术标签 | 深度学习 |
| 相关度评分 | ★★★★ 4.0 / 5.0 |
| 关键词 | 结温监测 温度敏感电参数 卷积神经网络 导通集电极电流 负载电流 |
语言:
中文摘要
基于温度敏感电参数（TSEP）的方法能够实现功率器件结温的精确监测（JTM）。然而，大多数温度敏感电参数易受负载电流和器件老化的影响而产生误差，从而降低了结温监测的准确性。为解决这一问题，本文提出了一种基于卷积神经网络（CNN）模型的结温监测方法，以应对这两个因素带来的不利影响。在该方法中，选择开通集电极电流（$I_{C}$）作为温度敏感电参数，并通过数学模型深入分析了开通集电极电流的温度特性。此外，通过大量双脉冲测试全面研究了开通集电极电流的参数相关性。考虑到实际中负载电流影响显著且频繁变化的情况，基于卷积神经网络减轻了负载电流的不利影响。最后，在单相整流器上进行了实验验证，证明了所提模型的有效性和准确性。
English Abstract
The temperature-sensitive electrical parameter (TSEP) -based method enables accurate junction temperature monitoring (JTM) of power devices. However, the majority of TSEPs are susceptible to errors due to the effects of load currents and device aging, reducing the accuracy of JTM. To address this, a JTM method based on a convolutional neural network (CNN) model is proposed to deal with the unfavorable effects of two factors. In this method, the turn-on collector current (IC) is selected as the TSEP, and the temperature characteristics of the turn-on IC are thoroughly analyzed by a mathematical model. Moreover, the parameter dependence of the turn-on IC is fully investigated with extensive double-pulse tests. Then, considering the significant effect and the frequent variations of load current in practice, the adverse effects of load current are mitigated based on the CNN. Finally, experimental verification is given to prove the effectiveness and accuracy of the proposed model based on a single-phase rectifier.
S
SunView 深度解读
该CNN结温监测技术对阳光电源功率器件热管理具有重要应用价值。可直接应用于ST系列储能变流器和SG系列光伏逆变器的SiC/GaN功率模块，通过实时监测IGBT/MOSFET结温实现预测性维护。相比传统TSEP方法，CNN自动特征提取克服了非线性补偿难题，无需额外传感电路即可从开关波形获取温度信息，适配三电平拓扑的多器件并联场景。该技术可集成至iSolarCloud平台实现智能诊断，通过结温趋势预测功率模块寿命，优化PowerTitan储能系统的热管理策略和降额设计，提升电动汽车OBC/充电桩在高温工况下的可靠性，降低现场故障率。