Experimental study of dynamic line rating applied to high-temperature low-sag conductors
This study presents an analysis of ampacity evaluation techniques in overhead transmission lines based on methods for calculating conductor capacity through static and dynamic line rating analysis. It is specifically applied to high-temperature low-sag (HTLS) conductors, comparing theoretical result...
| Autores: | , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad de Cantabria (UC) |
| Repositorio: | UCrea Repositorio Abierto de la Universidad de Cantabria |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.unican.es:10902/36015 |
| Acceso en línea: | https://hdl.handle.net/10902/36015 |
| Access Level: | acceso abierto |
| Palabra clave: | Ampacity HTLS Overhead lines Dynamic line rating IEEE CIGRE |
| Sumario: | This study presents an analysis of ampacity evaluation techniques in overhead transmission lines based on methods for calculating conductor capacity through static and dynamic line rating analysis. It is specifically applied to high-temperature low-sag (HTLS) conductors, comparing theoretical results with empirically obtained real-time measurements. International standard methodologies, such as those from the IEEE and CIGRE, are examined to assess the impact of meteorological conditions on the performance of overhead transmission lines. Theoretical analyses are contrasted with experimental results obtained through a physical simulation prototype designed specifically for this purpose. A comparative analysis is presented that evaluates the deviations in conventional ampacity calculation techniques based on conductor temperature, comparing them with their performance in experimental situations for High-Temperature Low-Sag conductors under high load demand. This research offers an analytical perspective for optimizing the performance of transmission lines through the use of high-density currents. The results analyze the accuracy of calculation methodologies under different load and environmental scenarios, identifying conditions with the highest deviations, to enable more efficient and secure management in high-demand scenarios. |
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