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
维普
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