  Residential transformer population is a critical type of asset that many
electric utility companies have been attempting to manage proactively and
effectively to reduce unexpected failures and life losses that are often caused
by transformer overloading. Within the typical power asset portfolio, the
residential transformer asset is often large in population, having lowest
reliability design, lacking transformer loading data and susceptible to
customer loading behaviors such as adoption of distributed energy resources and
electric vehicles. On the bright side, the availability of more residential
operation data along with the advancement of data analytics techniques have
provided a new path to further our understanding of local residential
transformer overloading risks statistically. This research developed a new
data-driven method to combine clustering analysis and the simulation of
transformer temperature rise and insulation life loss to quantitatively and
statistically assess the overloading risk of residential transformer population
in one area and suggest proper risk management measures according to the
assessment results. Case studies from an actual Canadian utility company have
been presented and discussed in detail to demonstrate the applicability and
usefulness of the proposed method.
