Abstract：The switching performance of medium and high voltage insulated gate bipolar transistor (IGBT) is important in converter design and converter performance, efficiency and lifetime improvement. Based on the experimental data of an off-line medium and high voltage power module test bench, the influence of environmental parameter such as gate voltage and gate resistor, collector-emitter voltage, collector current and device junction temperature on device switching characteristics was explored and an error back-propagation multi-layer feed-forward neural network model based on genetic algorithm optimization was built in this paper. The model has realized reliable predictions of IGBT switching characteristics such as switching time, switching losses, voltage overshoot and current spike under different environments with high precision.
陈娜, 李鹏, 江剑, 邓焰, 何湘宁. 中高压IGBT开关特性的遗传神经网络预测[J]. 电工技术学报, 2013, 28(2): 239-247.
Chen Na, Li Peng, Jiang Jian, Deng Yan, He Xiangning. Genetic Neural Network Prediction on Medium and High Voltage IGBT Switching Performance. Transactions of China Electrotechnical Society, 2013, 28(2): 239-247.
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