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With the growing energy demand and sustainable development in the energy sector, the limited conventional energy resources cannot fulfill the rising requirements, resulting in the acceleration of Renewable Energy Resources (RESs) in recent years. However, the rapid integration of RESs may lead to new challenges related to the power transfer ability of the conventional grid. The Dynamics Line Rating (DLR) solution was introduced to enhance the power flows for alternating current and direct current overhead transmission lines (OHLs) under the line’s maximum conductor temperature constraint. Based on the real-time weather conditions, DLR is usually higher than the Statics Line Rating calculated for conservative standard annual or seasonal conditions. This work aims to demonstrate the performance and reliability analysis of the DLR system on an example OHL using the IEEE-738 standard. In addition, to ensure a continuous operation in case of communication delay or data loss, we also consider a forecasted DRL model based on a numerical weather prediction model with Deep Learning Weather Prediction approach.

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Enhancing Power Flow of Overhead Transmission Lines with Dynamic Thermal Rating Estimation Based on Weather Forecasting Values

  • 会议论文

  • 发表日期：

    2026年

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  • Xuan Truong Nguyen,
  • Mai Quyen Hoang

摘要

With the growing energy demand and sustainable development in the energy sector, the limited conventional energy resources cannot fulfill the rising requirements, resulting in the acceleration of Renewable Energy Resources (RESs) in recent years. However, the rapid integration of RESs may lead to new challenges related to the power transfer ability of the conventional grid. The Dynamics Line Rating (DLR) solution was introduced to enhance the power flows for alternating current and direct current overhead transmission lines (OHLs) under the line’s maximum conductor temperature constraint. Based on the real-time weather conditions, DLR is usually higher than the Statics Line Rating calculated for conservative standard annual or seasonal conditions. This work aims to demonstrate the performance and reliability analysis of the DLR system on an example OHL using the IEEE-738 standard. In addition, to ensure a continuous operation in case of communication delay or data loss, we also consider a forecasted DRL model based on a numerical weather prediction model with Deep Learning Weather Prediction approach.