Predicting transformer temperature field based on physics-informed neural networks

Abstract

The safe operation of oil-immersed transformers is critical to the safety and stability of the power grid. As the operating time increases, the failure rate of oil-immersed transformers shows an increasing trend, posing serious challenges to safe operation. It is necessary to investigate the internal state of the oil-immersed transformer to improve the digital degree and achieve digitalisation and intelligent operation and maintenance. A physics-informed neural network (PINN) for oil-immersed transformers was introduced to reconstruct the temperature distribution inside the transformer. According to the approach, the loss function of the network would be optimised by incorporating physical constraint loss terms including heat transfer equations, initial conditions and boundary conditions. The results show that the method proposed can be used to reconstruct and predict the temperature field of transformers in a few seconds with satisfactory accuracy. In conclusion, the PINN proposed outperforms deep neural networks in terms of accuracy, reliability and interpretability, especially in data-poor cases.