Risk-Based Dynamic Thermal Rating in Distribution Transformers via Probabilistic Forecasting

TL;DR

This paper introduces a probabilistic framework for dynamically optimizing transformer thermal protection settings, enabling increased capacity utilization while managing overheating risk through probabilistic load and temperature forecasts.

Contribution

It proposes a novel probabilistic forecasting method using clustered quantile regression to optimize transformer protection settings dynamically based on historical data.

Findings

Achieved 10-12% additional capacity gain over static settings.

Successfully predicted hotspot temperature risk matching the chosen percentile.

Demonstrated robustness under realistic temperature forecast errors.

Abstract

Low voltage (LV) distribution transformers face accelerating demand growth while replacement lead times and costs continue to rise, making improved utilisation of existing assets essential. Static and conservative protection devices (PDs) in distribution transformers are inflexible and limit the available headroom of the transformer. This paper presents a probabilistic framework for dynamically forecasting optimal thermal protection settings. The proposed approach directly predicts the day-ahead scale factor which maximises the dynamic thermal rating of the transformer from historical load, temperature, and metadata using clustered quantile regression models trained on 644 UK LV transformers. Probabilistic forecasting quantifies overheating risk directly through the prediction percentile, enabling risk-informed operational decisions. Results show a 10--12\% additional capacity gain compared to static settings, with hotspot temperature risk matching the selected percentile, including under realistic temperature forecast errors. These results demonstrate a practical approach for distribution network operators to take advantage of PDs with adaptive settings to maximise capacity and manage risk on operational time scales.