Ambient Temperature-Dependent Parameterization of the IEC 60076-7 Top-Oil Thermal Model

Transformer top-oil temperature prediction is essential for reliable thermal assessment and lifetime management. Although the IEC 60076-7 loading guide model is widely adopted for this purpose, its thermal parameters are generally assumed to remain constant regardless of environmental conditions: an assumption that has received limited attention. This paper investigates the influence of ambient temperature on the IEC top-oil model parameters and proposes an adaptive ambient-dependent parameterization framework. A segmented optimization methodology is applied to field measurements from a 66 MVA, 225/26.4 kV power transformer to identify the optimal rated top-oil temperature rise and thermal exponent x over different ambient temperatures. The results reveal an approximately linear dependence of the optimized parameters on ambient temperature, suggesting that the thermal dynamics represented by the IEC model evolve with environmental conditions. Incorporating this dependency improves prediction accuracy, achieving a mean absolute error (MAE) of 2.03 °C and a root mean squared error (RMSE) of 2.56 °C on the test dataset, outperforming both the conventional IEC model with fixed parameters and a previously published adaptive approach. These findings offer a practical extension of the IEC model with applications in transformer monitoring, dynamic loading, and digital-twin-based asset management.