In response to the complex multi-physical field coupling relationships in transformers and the nonlinear characteristics of materials

existing state parameter distribution characteristic solving techniques struggle to balance model accuracy and real-time performance

making it difficult to meet the needs for global field visualization and real-time state assessment of transformers. This paper proposes an efficient computational method for the temperature distribution in transformer multi-physical field coupling. Initially

a transformer model is established through coupling electromagnetic

thermal and fluid multi-physics fields. Subsequently

a differential evolution algorithm is proposed to optimize the placement of the sensor. The improved Pix2pix network is built to bolster both the global and local computational precision by introducing multi-receptive field feature mining and multi-scale feature discrimination. Finally

a down-scaling technique based on Kriging interpolation is adopted to improve the spatial resolution of the temperature distribution

and to realize the refined calculation of the multi-physical field coupled temperature distribution in transformers. Validation through transformer simulation models and actual transformer data shows that the proposed method achieves a temperature distribution calculation accuracy of over 98% in simulation models and over 85% in actual transformers

with computation time consistently under 6 seconds. The method presented in this paper balances computational accuracy and efficiency

providing a robust technical support for the safe operation of transformers.