中国电机工程学报2024，Vol.44Issue(7)：2897-2909,后插32,14.DOI:10.13334/j.0258-8013.pcsee.223165
基于U-net神经网络的油浸式变压器绕组流-热耦合快速计算
Fast Calculation of Flow-thermal Coupling Model of Oil-immersed Transformer Windings Based on U-net Neural Network
摘要
Abstract
In this paper,a fast calculation method based on U-net neural network training is proposed for the problem of long simulation time of temperature rise of large oil-immersed transformer winding by traditional numerical methods,which can rapidly predict transformer winding temperature rise and hot spot.First,the input variables are screened according to the flow-thermal coupling principle,and the output results under different operating conditions are calculated using the flow-thermal coupling method and made into a training set and a test set.Then,the three hyperparameters that have the most significant influence on the network training are discussed in detail;meanwhile,the normalized training set is input into the U-net neural network for training and the optimal combination of hyperparameters is set.Finally,the prediction set is input into the trained model for prediction calculation and anti-normalization operation.In conclusion,the difference between the predicted winding hot spot and the Fluent simulation result is only 0.44 K.The single simulation time is shortened from 200 s to 0.07 s.Moreover,the average error between the prediction result and the experimental temperature is 2.31 K at the maximum and 0.98 K at the minimum,and the prediction variance is about 0.31.The results show that the method can be used to obtain the temperature and hot spot of oil-immersed transformer winding quickly,and can meet the real-time simulation requirements of transformer temperature hot spot digital twin.关键词
U-net神经网络/流热耦合/绕组温升/快速计算/数字孪生Key words
U-net neural network/flow-thermal coupling/winding temperature rise/fast calculation/digital twin分类
动力与电气工程引用本文复制引用
刘云鹏,高艺倩,刘刚,胡万君,王文浩,王博闻,高成龙..基于U-net神经网络的油浸式变压器绕组流-热耦合快速计算[J].中国电机工程学报,2024,44(7):2897-2909,后插32,14.基金项目
国网浙江省电力有限公司科技项目(5211DS220005).Project Supported by Science and Technology Project of State Grid Zhejiang Electric Power Co.,Ltd.(5211DS220005). （5211DS220005）
