Deep Learning Model for Magnetic Field Prediction of Transformers Under Overvoltage Conditions

ABSTRACT The transformer is an important equipment in power systems. However, prolonged abnormal conditions can lead to significant damage of the transformer equipment. The current finite element analysis (FEA) method for calculating the internal physical field of transformers is time‐consuming, limiting its practicality for fast simulation. This paper focuses on predicting the internal magnetic fields of transformers under overvoltage conditions, which cause irregular changes in the transformer magnetic fields due to overvoltage. Simulation datasets of transformer magnetic field under overvoltage conditions were acquired via the COMSOL software. Subsequent analysis elucidated the influence of overvoltage parameters on the electrical characteristics of transformers. Furthermore, the dimensionality of input features relevant to magnetic field prediction was expanded. Convolutional neural network (CNN) model was then employed to forecast the internal magnetic fields of transformers under overvoltage conditions. Experimental results were compared with Random Forest (RF), eXtreme Gradient Boosting (XGBoost) and deep neural network (DNN) models, demonstrating the efficiency of deep learning methods in predicting transformer magnetic fields under overvoltage conditions.