Data-Driven Thermal Modeling of Residential Service Transformers

Sales of privately-owned plug-in electric vehicles (PEVs) are projected to increase dramatically in coming years and their charging will impact residential service transformer loads. Transformer life expectancy is strongly related to the cumulative effects of internal winding temperatures, which are a function of loading. Thermal models exist (e.g., IEEE Standard C57.91) for predicting these internal temperatures, the most sophisticated being the Annex G model. While this model has been validated with measurements from large power transformers, small residential service transformers have been given less attention. Given increasing PEV loads, a better understanding of service transformer aging could be useful in replacement planning processes. Empirical data from this paper indicate that the Annex G model over-estimates internal temperatures in small 25 kVA 65 °C rise mineral-oil-immersed transformers. This paper presents an alternative model to Annex G by using a genetic program. Empirical results using a thermally-instrumented transformer suggest that this model is both simpler and more accurate at tracking empirical transformer data. We conclude that one can use a simple thermal model in combination with data from advanced metering infrastructure to more accurately estimate service transformer lifetimes, and thus better plan for transformer replacement.