Distribution transformers (DTs) are deemed fundamental and high-priced equipment for power grids, and their failure influences grid reliability. The continuity of DT operation highly depends on its insulation conditions and following that to the hotspot temperature (HST). In this article, a novel formula is proposed for HST prediction of DTs by considering the effective electrical and mechanical parameters on heat dissipation capacity. Additionally, complete and accurate 3D modeling based on computational fluid dynamics (CFD) is presented to validate the proposed novel formula for HST prediction. In this modeling, the conservator, which plays a key role in the HST value, is accurately modeled. Optical fiber sensors are utilized in the studied 500-kVA DT to verify the accuracy of the proposed HST prediction formula during the experimental temperature rise test. Experimental results show that the proposed formula is highly accurate and has an acceptable correlation with the empirical values. The root-mean-square error, mean error, and average error percentage (AEP) of the proposed formula are 0.3 °C, 0.3 °C, and 0.37%, respectively, in HST transient values, and 0.3 °C, 0.3 °C, and 0.29% in HST steady-state values, which substantiate the precision and proficiency of the proposed HST prediction formula rather than IEC and IEEE equations. Finally, a thermal camera is employed to verify the results of CFD-based 3D modeling in top-oil temperature, bottom-oil temperature, and conservator oil temperature during the experimental tests. According to the measurement results, temperatures of CFD-based 3D simulation and thermal camera in the aforementioned three points are in good agreement with each other and AEP is less than 1.4%, which indicates the accuracy and efficiency of the proposed 3-D modeling.

In the energy distribution networks, the most important and valuable equipment is oil‐immersed distribution transformers. Besides, due to the key role of these transformers and their multiplicity, their lifetime monitoring is inevitable. The life of a transformer depends on the weakest solid insulation material (i.e. paper insulation). On the other hand, monitoring the transformer insulation status requires accurate information to be available about the oil temperature at every moment. Therefore, it is important to control and predict the oil temperature rise in the transformer. In this study, a new model based on fundamental heat transfer theory is proposed for thermal behaviour prediction of top oil of indoor distribution transformers using the concept of thermal resistance, namely electro‐thermal resistance model (E‐TRM). In E‐TRM, the thermal resistance network is formed by following three‐dimensional heat transfer path and assigning thermal resistance to each path. To evaluate the proposed E‐TRM, the results of this model are verified with experimental results. Moreover, the E‐TRM is used to predict the thermal behaviour of the indoor transformer in the overloading condition. At the end, the transformer loss of life is estimated based on the oil temperature and a normal cyclic overloading strategy is presented for overloading management.