The insulated-gate bipolar transistor (IGBT) is one of the most widely used power transistors in switching and industrial control systems. Its actual junction temperature plays a critical factor in determining the dynamic performance, reliability and life-time of the device. Although some noninvasive measurement methods such as optical and physical contact methods may be used to estimate the junction temperature, the measurement accuracy is very sensitive to the measured position. Therefore, the prediction using cuckoo search-based extreme learning machine for junction temperature is developed to reach a high-accuracy solution without measured-location sensitivity. Firstly, the accelerated aging and single pulse tests in IGBT are implemented to collect the IGBT failure related parameters, e.g. collector-emitter saturation voltage (V_CE(sat)), junction temperature, collector current (I_c) and the aging cycles number. With the curved surface fitting for the collected data, the relationship between the junction temperature and the other parameters can be formed. Based on the extreme learning machine optimized by the improved Cuckoo Search method, called ICS-ELM, V_CE(sat), I_c and the aging cycles number are taken as input, and the output is the predicted junction temperature. The performance results reveal that the determination coefficient (R²) by ICS-ELM model achieves the optimal value, i.e. 0.9975, which is superior to the curved surface fitting method, Cuckoo search optimizing extreme learning machine, support vector machine and extreme learning machine.

绝缘栅双极晶体管 (IGBT) 是开关和工业控制系统中使用最广泛的功率晶体管之一。其实际结温是决定器件动态性能、可靠性和使用寿命的关键因素。虽然一些无创测量方法如光学和物理接触方法可用于估计结温，但测量精度对测量位置非常敏感。因此，开发了使用基于布谷鸟搜索的极限学习机对结温进行预测，以达到没有测量位置敏感性的高精度解决方案。首先对IGBT进行加速老化和单脉冲测试，采集IGBT失效相关参数，如集电极-发射极饱和电压（V _CE(sat) )、结温、集电极电流 ( I _c ) 和老化循环数。通过对采集数据进行曲面拟合，可以形成结温与其他参数之间的关系。基于改进的Cuckoo Search方法优化的极限学习机，称为ICS-ELM，以V _CE(sat)、I _c和老化周期数为输入，输出为预测的结温。性能结果表明，决定系数 (R ²)通过ICS-ELM模型达到最优值，即0.9975，优于曲面拟合方法、Cuckoo搜索优化极限学习机、支持向量机和极限学习机。