摘要
实时监测功率变流器中支撑电容的老化状态,及时发现并更换存在缺陷的电容,对提高功率变换器的可靠性具有重要意义。基于相关电压电流数据,通过建立数据集,确定网络模型参数和模型训练,最终得到基于CNN-LSTM的神经网络模型,并通过不同工况下的数据集对神经网络模型的准确性进行了验证。结果表明,该模型可对电容容值进行可靠预测。
It is of great significance to monitor the aging state of the supporting capacitors in the power converter in real time and to find and replace the defective capacitors in time.In this paper,based on the relevant voltage and current data,through the establishment of data sets,the network model parameters and model training are determined.Finally,the neural network model based on CNN-LSTM is obtained.The accuracy of the neural network model is verified by the data sets under different working conditions.The results show that the model can reliably predict the capacitance value.
作者
杨培盛
付宇
李鸿飞
初开麒
王梦谦
李政达
Yang Peisheng;Fu Yu;Li Hongfei;Chu Kaiqi;Wang Mengqian;Li Zhengda(Jinan Rail Transit Group Construction Investment Co.,Ltd.,Jinan 250014,China;CRRC Qingdao Sifang Rolling Stock Research Institute Co.,Ltd.,Qingdao 266033,China)
出处
《电子技术应用》
2021年第9期16-19,共4页
Application of Electronic Technique
