电气技术2025，Vol.26Issue(3)：42-48,7.
基于神经网络的绝缘栅双极型晶体管开关损耗预测
Insulated gate bipolar transistor switching loss prediction based on neural network
王长华 1李祥雄 1梁顺发 1陈荣东1
作者信息
- 1. 顺特电气设备有限公司,广东 佛山 528300
- 折叠
摘要
Abstract
Aiming at the disadvantages that numerous insulated gate bipolar transistor(IGBT)switching loss are difficult to accurately measure online in the cascaded energy storage application area,switching loss prediction model is established based on the error back propagation neural network.Firstly,dynamic test system of switching loss is built with cascaded H bridge power module,the massive switching loss data is obtained with changing the direct current bus voltage,alternating current and coolant temperature of power module.3 main factors including collector-emitter voltage,collector current and device junction temperature are taken as the input of IGBT switching loss prediction model.The particle swarm optimization is used to optimize the initial weight and threshold of prediction model,improving prediction accuracy and accelerating the convergence of learning laws.The optimized performance of this model is compared and analyzed with the prediction model that the initial weight and threshold are given randomly.The results show that the prediction accuracy of the model proposed in this paper is higher.The maximum percentage error for 50 sets of random validation data is 3.3%.关键词
绝缘栅双极型晶体管(IGBT)/开关损耗预测/神经网络/粒子群优化算法Key words
insulated gate bipolar transistor(IGBT)/switching loss prediction/neural network/particle swarm optimization引用本文复制引用
王长华,李祥雄,梁顺发,陈荣东..基于神经网络的绝缘栅双极型晶体管开关损耗预测[J].电气技术,2025,26(3):42-48,7.
