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This research aims to optimize thermal protection settings in low voltage distribution transformers through probabilistic forecasting.

Risk-based dynamic thermal rating in distribution transformers via probabilistic forecasting

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Randomized trial examines dynamic thermal rating to enhance transformer capacity while managing risk.

This research aims to optimize thermal protection settings in low voltage distribution transformers through probabilistic forecasting.
Developed a probabilistic framework for forecasting transformer thermal protection settings.
Used clustered quantile regression models trained on data from 644 UK low voltage transformers.
Predicted day-ahead scale factors from historical load, temperature, and metadata.
Achieved a 10-12% increase in capacity compared to static settings.
Hotspot temperature risk aligned with the selected prediction percentile.
Validated under realistic temperature forecast errors.

Angus et al. (2026) studied this question.

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