Physics-Informed Neural Network Framework for Dynamic Thermal Modeling of Power Transformers Under Extreme Climate Conditions

Power transformers are essential components of electric power transmission systems operating under diverse environmental conditions. Their thermal performance strongly influences insulation aging, operational reliability, and power system stability. Conventional empirical thermal models, including the IEC 60076-7 standard, often exhibit limited predictive capability under extreme climate conditions. This study proposes a Physics-Informed Neural Network (PINN) integrated with an advanced Multi-Physics Transformer Thermal Model (MTTM) to improve transformer thermal prediction while preserving physical consistency. The proposed framework identifies research gaps in transformer thermal modeling, optimizes the PINN training strategy to reduce computational cost and improve predictive performance, evaluates integration with the OSIsoft PI System architecture, and validates the model using geographically diverse datasets. A case study conducted on a 500 MVA, 400/220 kV transformer demonstrated a root mean square error of 1.12°C and a mean absolute error of 0.89°C, representing a 48% improvement over the IEC 60076-7 standard. Although PINN training requires significant GPU resources, the trained model performs real-time inference within 12 milliseconds. Validation using multiple transformer datasets confirmed the robustness, scalability, and practical applicability of the proposed framework for dynamic transformer rating and asset management, with an estimated annual economic benefit of approximately USD 1.45 million.

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