Abstract Junction-to-ambient thermal network model is an important foundation for evaluating heat-sinking capability and remaining life of power devices, because it can estimate the variation of power device junction temperature with the ambient temperature. However, the traditional junction-to-ambient thermal network model generally assumes that the power devices works in the stable ambient environment, ignoring the random effect of thermal convection on the power device. As a result, it is difficult to effectively describe the variation characteristic of junction temperature. In this paper, a new junction-to-ambient thermal network model of power devices considering the randomness of thermal convective environment was proposed. In the proposed model, the samples of convective thermal resistances between the heat sink and ambient environment were calculated with the historical power loss and temperature data of power device. Then, the wavelet packet transform and Markov chain method were employed to randomly simulate the convective thermal resistance. Finally, the random fluctuation of power device junction temperature can be estimated under the specified current and ambient temperature conditions. A random thermal convection experimental platform was designed and the power MOSFETs were used as the experimental subjects. The experimental results show that the proposed model can effectively describe the uncertain change of power device junction temperature under the action of random thermal convection, and can provide important data support for the development of power device thermal safety evaluation and lifetime prediction methods.
Jiang Xin,Ying Zhanfeng,Wan Meng等. Junction-to-Ambient Thermal Network Model of Power Devices Considering Randomness of Thermal Convective Environment[J]. Transactions of China Electrotechnical Society, 2021, 36(14): 3090-3100.
[1] Dusmez S, Ali S H, Heydarzadeh M, et al.Aging precursor identification and lifetime estimation for thermally aged discrete package silicon power switches[J]. IEEE Transactions on Industry Applica- tions, 2017, 53(1): 251-260. [2] Fleetwood D M, Winokur P S, Dodd P E.An overiview of radiation effects on electronics in the space telecommunications environment[J]. Microelec- tronics Reliability, 2000, 40(1): 17-26. [3] Ning Puqi, Li Lei, Wen Xuhui.A hybrid Si IGBT and SiC MOSFET module development[J]. CES Transa- ctions on Electrical Machines and Systems, 2017, 1(3): 360-366. [4] Xu Yang, Chen Hao, Sen Lü, et al.Thermal model for power converters based on thermal impedance[J]. Journal of Power Electronics, 2013, 13(6): 1080-1089. [5] 陈明, 胡安, 唐勇, 等. 绝缘栅双极型晶体管传热模型建模分析[J]. 高电压技术, 2011, 37(2):453-459. Chen Ming, Hu An, Tang Yong, et al.Modeling analysis of IGBT thermal model[J]. High Voltage Engineering, 2011, 37(2): 453-459. [6] 王莉娜, 邓洁, 杨军一, 等. Si和SiC功率器件结温提取技术现状及展望[J]. 电工技术学报, 2019, 34(4): 703-716. Wang Lina, Deng Jie, Yang Junyi, et al.Junction temperature extraction methods for Si and SiC power devices-a review and possible alternatives[J]. Transactions of China Electrotechnical Society, 2019, 34(4): 703-716. [7] 姚芳, 胡洋, 唐圣学, 等. 风电变流器IGBT模块工作结温估算研究[J]. 电机与控制学报, 2018, 22(8): 26-33. Yao Fang, Hu Yang, Tang Shengxue, et al.Research on the junction temperature estimation of IGBT modules in wind power converters[J]. Electric Machines and Control, 2018, 22(8): 26-33. [8] Ma Ke, Bahman A S, Beczkowski S, et al.Complete loss and thermal model of power semiconductors including device rating information[J]. IEEE Transa- ctions on Power Electronics, 2015, 30(5): 2556-2569. [9] 刘宾礼, 罗毅飞, 肖飞, 等. 适用于器件级到系统级热仿真的IGBT传热模型[J]. 电工技术学报, 2017, 32(13): 1-13. Liu Binli, Luo Yifei, Xiao Fei, et al.IGBT thermal model for thermal simulation of device to system[J]. Transactions of China Electrotechnical Society, 2017, 32(13): 1-13. [10] Zhang Jun, Du Xiong, Wu Yu, et al.Thermal parameter monitoring of IGBT module using case temperature[J]. IEEE Transactions on Power Elec- tronics, 2019, 8(34): 7942-7956. [11] 万萌, 应展烽, 张伟. 分立型功率MOSFET结温估计的非线性热网络模型和参数辨识方法[J]. 电工技术学报, 2019, 34(12): 2477-2488. Wan Meng, Ying Zhanfeng, Zhang Wei.Nonlinear thermal network model and parameter identification method for junction temperature estimation of discrete power MOSFET[J]. Transactions of China Electrotechnical Society, 2019, 34(12): 2477-2488. [12] Luo Zhaohui, Ahn H, Elnokali M.A thermal model for insulated gate bipolar transistor module[J]. IEEE Transactions on Power Electronics, 2004, 19(4): 902-907. [13] 汪波, 胡安, 唐勇. 基于电热模型的IGBT结温预测与失效分析[J]. 电机与控制学报, 2012, 16(8): 87-93. Wang Bo, Hu An, Tang Yong.Junction temperature forecast and failure analysis of IGBT based on electrothermal model[J]. Electric Machines and Control, 2012, 16(8): 87-93. [14] Du Xiong, Li Gaoxian, Sun Pengju, et al.A hybrid modulation method for lifetime extension of power semiconductors in wind power converters[C]//2015 IEEE Applied Power Electronics Conference and Exposition (APEC), Charlotte, USA, 2015: 2565-2570. [15] 姚芳, 胡洋, 李静, 等. 基于结温监测的风电IGBT热安全性和寿命损耗研究[J]. 电工技术学报, 2018, 33(9): 2024-2033. Yao Fang, Hu Yang, Li Jing, et al.Study on thermal safety and lifetime consumption of IGBT in wind- power converters based on junction temperature monitoring[J]. Transactions of China Electrotechnical Society, 2018, 33(9): 2024-2033. [16] Blinov A, Vinnikov D, Lehtla T.Cooling methods for high-power electronic systems[J]. Scientific Journal of Riga Technical University Power Electrical Engin- eering, 2011, 29(1): 79-86. [17] Shabny Y.Heat transfer: thermal management of electronics[M]. Boca Raton: CRC Press, 2010. [18] 吴岩松, 罗皓泽, 李武华, 等. 用于IGBT模块结温预测的热-电耦合模型研究[J]. 电工电能新技术, 2014, 33(3): 13-17, 65. Wu Yansong, Luo Haoze, Li Wuhua, et al.Researches on electrothermal model for junction temperature prediction in IGBT modules[J]. Advanced Techno- logy of Electrical Engineering and Energy, 2014, 33(3): 13-17, 65. [19] Lemmens J, Vanassche P, Driesen J.Optimal control of traction motor drives under electrothermal con- straints[J]. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2014, 2(2): 249-263. [20] Ciappa M, Fichtner W, Kojima T, et al.Extraction of accurate thermal compact models for fast electro- thermal simulation of IGBT modules in hybrid electric vehicles[J]. Microelectronics Reliability, 2005, 45(9): 1694-1699. [21] 王希平, 李志刚, 姚芳. 模块化多电平换流阀IGBT器件功率损耗计算与结温探测[J]. 电工技术学报, 2019, 34(8): 1636-1646. Wang Xiping, Li Zhigang, Yao Fang.Power loss calculation and junction temperature detection of IGBT devices for modular multilevel valve[J]. Transactions of China Electrotechnical Society, 2019, 34(8): 1636-1646. [22] 万萌, 应展烽, 张旭东. 基于功率器件壳温估计的逆变电路动态限流方法[J]. 电机与控制学报, 2020, 24(5): 89-98, 106. Wan Meng, Ying Zhanfeng, Zhang Xudong.A dynamic current limiting method of inverter circuit based on case temperature estimation of power devices[J]. Electric Machines and Control, 2020, 24(5): 89-98, 106. [23] 索超男, 张慧, 赵雄文. 小波基在低压电力线信信道有色背景噪声建模中的应用研究[J]. 电力系统保护与控制, 2017, 45(4): 121-125. Suo Chaonan, Zhang Hui, Zhao Xiongwen.Research on the application of wavelet basis functions on modeling of colored background noise for low- voltage power line channels[J]. Power System Protection and Control, 2017, 45(4): 121-125. [24] 王慧琴. 小波分析与应用[M]. 北京: 北京邮电大学出版社, 2010. [25] 袁贝尔, 应展烽, 齐保军, 等. 高压碳纤维复合芯导线输电线路热过载运行的风险评估方法[J]. 电力系统自动化, 2018, 42(1): 111-117. Yuan Beier, Ying Zhanfeng, Qi Baojun, et al.Over- heating risk assessment method for high voltage transmission line using aluminum conductor com- posite core[J]. Automation of Electric Power Systems, 2018, 42(1): 111-117. [26] 冯凯, 应展烽, 吴军基, 等. 基于小波包变换和峰式马尔科夫链的风速短期预测[J]. 南京理工大学学报, 2014, 38(5): 639-643, 657. Feng Kai, Ying Zhanfeng, Wu Junji, et al.Short-term wind speed forecast based on wavelet packet decom- position and peak-type Markov chain[J]. Journal of Nanjing University of Science and Technology, 2014, 38(5): 639-643, 657. [27] 任永峰, 薛宇, 云平平, 等. 马尔可夫预测的多目标优化储能系统平抑风电场功率波动[J]. 电力系统自动化, 2020, 44(6): 67-76. Ren Yongfeng, Xue Yu, Yun Pingping, et al.Multi- objective optimization of energy storage system with Markov prediction for power fluctuation suppression of wind farm[J]. Automation of Electric Power Systems, 2020, 44(6): 67-76. [28] 朱晨曦, 张焰, 严正, 等. 采用改进马尔科夫链蒙特卡洛法的风电功率序列建模[J]. 电工技术学报, 2020, 35(3): 577-589. Zhu Chenxi, Zhang Yan, Yan Zheng, et al.A wind power time series modeling method based on the improved Markov chain Monte Carlo method[J]. Transactions of China Electrotechnical Society, 2020, 35(3): 577-589. [29] 张玲华, 郑宝玉. 随机信号处理[M]. 北京: 清华大学出版, 2003. [30] 马晓慧, 邹传云. 数字超宽带信号的功率谱密度[J].电子与信息学报, 2007, 29(8): 1877-1881. Ma Xiaohui, Zou Chuanyun.Power spectral density of digital ultra wide-band signals[J]. Journal of Electronics and Information Technology, 2007, 29(8): 1877-1881. [31] 关健, 郜峰利, 张弛, 等. 间歇混沌合成1/f噪声的相关特性分析[J]. 电子学报, 2016, 44(6): 1389-1393. Guan Jian, Gao Fengli, Zhang Chi, et al.Analysis of characteristics of 1/f noise generated by inter mit- tency[J]. Acta Electronica Sinica, 2016, 44(6): 1389-1393.
2021, Vol. 36 
