Fast Calculation of Flow-thermal Coupling Model of Oil-immersed Transformer Windings Based on U-net Neural Network

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. ©2024 Chin.Soc.for Elec.Eng.