Power transformers, the workhorses of electric power transmission systems, operate under extreme climate conditions and have a considerable influence on the steady-state stability of the power network. The power transformer thermal performance is of paramount importance in assuring reliability and preventing thermal insulation aging. In the past, extensive research has been conducted into transformer thermal capability modelling. However, conventional empirical models, such as the power transformer thermal model outlined in the IEC 60076-7 standard, present difficulties in dealing with extreme climate scenarios. A physics-informed neural network (PINN) combined with the advanced Multi-Physics Transformer Thermal Model (MTTM) has been proposed to enhance the transformer thermal performance predictions.

A case study has been performed on a realistic 500 MVA, 400/220 kV transformer fed from the advanced data acquisition system of the OSIsoft PI System architecture developed by CIGRE Working Group B3.37 over a year-and-half period. The results indicated one of the best prediction accuracies at root mean square error 1.12°C and mean absolute error 0.89°C. In addition, the accuracy of the developed model has improved 48% over the IEC 60076-7 standard and confirmed the economic benefit of dynamic transformer rating based on thermal model with solar radiation impact and moisture content eff. Although the training of the PINN showed signs of excessive computational demands on the optimal use of the GPU membership from an Edge computer, the traditional alternative of using comprehensive MTTM, achieved a rapid 12-millisecond inference time. To add further validation, the proposed model was trained on three additional datasets based on two different transformer designs and reporting architecture. The predicted accuracy compared to the different MTMMs illustrated the reliability of the proposed model on various large scale hybrid transformers.

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