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| Genetic Neural Network Prediction on Medium and High Voltage IGBT Switching Performance |
| Chen Na, Li Peng, Jiang Jian, Deng Yan, He Xiangning |
| Zhejiang University National Laboratory of Power Electronics, Hangzhou 310027 |
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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.
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Received: 21 July 2011
Published: 28 November 2013
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2013, Vol. 28 
