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 and Gianluca Bontempi
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Gian Marco Paldino: Machine Learning Group, Université Libre de Bruxelles, 1050 Bruxelles, Belgium
Fabrizio De Caro: Dipartimento di Ingegneria, Università degli Studi del Sannio, 82100 Benevento, Italy
Jacopo De Stefani: Machine Learning Group, Université Libre de Bruxelles, 1050 Bruxelles, Belgium
Alfredo Vaccaro: Dipartimento di Ingegneria, Università degli Studi del Sannio, 82100 Benevento, Italy
Domenico Villacci: Dipartimento di Ingegneria Industriale, Università degli Studi di Napoli Federico II, 80125 Napoli, Italy
Gianluca Bontempi: Machine Learning Group, Université Libre de Bruxelles, 1050 Bruxelles, Belgium

Energies, 2022, vol. 15, issue 6, 1-17

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.

Keywords: dynamic thermal line rating; digital twin; data-driven; estimation; forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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