Research on temperature rise calculation and hot spot temperature inversion method for oil immersed transformer based on magnetic - thermal - fluid

Abstract

The hot spot temperature of oil-immersed transformer winding is an important factor affecting the aging of material insulation. In this paper, a magnetic field simulation model is established based on the electrical and structural parameters of the oil-immersed transformer, and the loss distribution characteristics of each wall of the transformer core, winding and fuel tank are accurately calculated by using the finite element simulation software. The simulation model of transformer fluid- thermal field is established, the simulation results of transformer thermal field are obtained, and the temperature distribution of oil-immersed transformer core and winding and the flow velocity around it are obtained. According to the simulation results of thermal field, the characteristic temperature measuring points with strong correlation between tank wall and winding temperature were determined. The inversion models of tank wall and winding hot spot temperature were established by using the support vector regression and BP neural network algorithm respectively by central composite design method. The results show that the correlation coefficient of support vector regression algorithm in predicting winding hot spot temperature reaches 0.98, and the relative error between the model predicted value and the real value is less than 8%, which is more accurate than BP neural network. The above research provides the theoretical basis and technical support for real-time monitoring of oil-immersed transformer winding hot spot temperature.