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.

利用机器学习技术和超局部天气预报数据，本研究提出了一种新颖的方法来解决传统动态线路额定容量（DLR）方法中遇到的挑战，采用传感器数据无法灵活应对快速变化的天气条件等问题，进而提高可再生能源与输电网的集成效率和可靠性。通过整合地形数据，该方法还能增强预测准确性，考虑电力线周围的地形特征和障碍物。通过在爱沙尼亚进行案例研究，论文展示了所提出方法在实际场景中的有效性，从而完善了过去传感器基础方法的局限性，并为可再生能源与输电系统的集成问题做出了贡献。