Dynamic Line Rating using Hyper-local Weather Predictions: A Machine Learning Approach

TL;DR

This work tackles the challenge of expanding transmission capacity for renewable energy by replacing sensor-heavy direct DLR with a machine-learning framework that leverages hyper-local weather forecasts and terrain data to predict span-level DLR with confidence intervals. By integrating dynamic and statistical downscaling, a multi-input ML model (including a Gaussian Mixture Model) outputs probabilistic wind components and CI-based DLR for every span, enabling risk-aware operation. A year-long Estonia case study demonstrates improved wind-speed predictions, CI coverage, and substantial capacity gains at the span level, while hotspot analysis reveals benefits from per-span temperature limits. The approach offers a scalable, sensor-light path to higher grid efficiency and reliability, with practical implications for real-time transmission planning and contingency management.

Abstract

Dynamic Line Rating (DLR) systems are crucial for renewable energy integration in transmission networks. However, traditional methods relying on sensor data face challenges due to the impracticality of installing sensors on every pole or span. Additionally, sensor-based approaches may struggle predicting DLR in rapidly changing weather conditions. This paper proposes a novel approach, leveraging machine learning (ML) techniques alongside hyper-local weather forecast data. Unlike conventional methods, which solely rely on sensor data, this approach utilizes ML models trained to predict hyper-local weather parameters on a full network scale. Integrating topographical data enhances prediction accuracy by accounting for landscape features and obstacles around overhead lines. The paper introduces confidence intervals for DLR assessments to mitigate risks associated with uncertainties. A case study from Estonia demonstrates the practical implementation of the proposed methodology, highlighting its effectiveness in real-world scenarios. By addressing limitations of sensor-based approaches, this research contributes to the discourse of renewable energy integration in transmission systems, advancing efficiency and reliability in the power grid.