Junction temperature prediction of IGBT based on neural network

In order to solve the problem of insulated gate bipolar transistor (IGBT) junction temperature prediction resulted from the nonlinear relationship among junction temperature, collector current and saturation drop voltage under large collector current, a test system was designed to measure the saturation drop voltage under different collector current and temperature. The curve among junction temperature, collector current and saturation drop voltage was obtained, and the curve was analyzed. Back propagation (BP) neural network model and radical basis function(RBF) neural network model coded in Matlab software were established. It is found that the relative prediction errors and confidence interval with 90% confidence level of two neural network models are smaller than that of polynomial fitting model, indicating that the neural network models provide more accurate prediction results than polynomial fitting model, and BP neural network is more accurate than RBF neural network, so the BP neural network is the preferential model. © 2019, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.