A Digital Twin Approach for Improving Estimation Accuracy in Dynamic Thermal Rating of Transmission Lines

Gian Marco Paldino*, Fabrizio De Caro, Jacopo De Stefani, Alfredo Vaccaro, Domenico Villacci, Gianluca Bontempi

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

The limitation of transmission lines thermal capacity plays a crucial role in the safety and reliability of power systems. Dynamic thermal line rating approaches aim to estimate the transmission line’s temperature and assess its compliance with the limitations above. Existing physics-based standards estimate the temperature based on environment and line conditions measured by several sensors. This manuscript shows that estimation accuracy can be improved by adopting a data-driven Digital Twin approach. The proposed method exploits machine learning by learning the input–output relation between the physical sensors data and the actual conductor temperature, serving as a digital equivalent to physics-based standards. An experimental assessment on real data, comparing the proposed approach with the IEEE 738 standard, shows a reduction of 60% of the Root Mean Squared Error and a decrease in the maximum estimation error from above 10 °C to below 7 °C. These preliminary results suggest that the Digital Twin provides more accurate and robust estimations, serving as a complement, or a potential alternative, to traditional methods.
Original languageEnglish
Article number2254
JournalEnergies
Volume15
Issue number6
DOIs
Publication statusPublished - 2022

Keywords

  • data-driven
  • digital twin
  • dynamic thermal line rating
  • estimation
  • forecasting

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