# Prediction of Magnetic Fields in Single-Phase Transformers Under Excitation Inrush Based on Machine Learning

## TL;DR

This paper introduces a machine learning method to predict magnetic fields in transformers during excitation inrush, offering faster and accurate monitoring for smart grids.

## Contribution

A deep neural network model is proposed for fast and accurate magnetic field prediction in transformers under excitation inrush.

## Key findings

- The DNN model achieved a 4.02% mean absolute percentage error in magnetic field prediction.
- The model is 46.68 times faster than traditional finite element analysis for single predictions.
- Machine learning is validated as effective for transformer magnetic field monitoring.

## Abstract

With the digital transformation of power systems, higher demands are being placed on smart grids for the timely and precise acquisition of the status of transmission and transformation equipment during operational and maintenance processes. When a transformer is energized under no-load conditions, an excitation inrush phenomenon occurs in the windings, posing a hazard to the stable operation of the power system. A machine learning approach is proposed in this paper for predicting the internal magnetic field of transformers under excitation inrush condition, enabling the monitoring of transformer operation status. Experimental results indicate that the mean absolute percentage error (MAPE) for predicting the transformer’s magnetic field using the deep neural network (DNN) model is 4.02%. The average time to obtain a single magnetic field data prediction is 0.41 s, which is 46.68 times faster than traditional finite element analysis (FEA) method, validating the effectiveness of machine learning for magnetic field prediction.