A Data-Driven Residential Transformer Overloading Risk Assessment Method

TL;DR

This paper introduces a data-driven approach combining clustering and simulation techniques to assess residential transformer overloading risks, aiding utilities in proactive risk management.

Contribution

It presents a novel method integrating clustering analysis with transformer temperature and insulation life simulations for risk assessment.

Findings

Effective risk assessment demonstrated on real utility data

Method identifies high-risk transformers for targeted maintenance

Supports proactive management to reduce failures

Abstract

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.