18th International Conference on Probabilistic Methods Applied to Power Systems - PMAPS 2024, Auckland (Nueva Zelanda). 24-27 junio 2024
Resumen:
In the context of global climate goals and the transition to sustainable energy, modern energy transportation and distribution systems play a crucial role. Electricity transportation and distribution systems would not function without power lines. One of the most challenges facing global power cable asset managers is efficiently managing the enormous and costly network of cables; most are getting closer or beyond their intended lifespan. Since HVDC systems are more economical and technically superior to HVAC systems for transmission over long distances, they have become increasingly important in the Power system. HVDC is preferred across 300–800 km for cable-based hookups and direct transmission schemes. This study aims to conduct a review study of the asset management strategies used for HVDC systems. Also, it explores the challenges and most recent advancements in asset management systems incorporating machine learning. Then, several machine learning algorithms used in recent studies are examined for asset management in power system applications.
Resumen divulgativo:
Este estudio revisa la gestión de activos de sistemas HVDC, destacando desafíos y avances en aprendizaje automático. Cubre tecnología HVDC, dificultades en gestión de activos, estrategias existentes y revisa aplicaciones de aprendizaje automático en sistemas HVDC, proponiendo la integración futura de sistemas de gestión inteligente.
Palabras clave: Power Systems, High Voltage Direct Current (HVDC), Artificial Intelligence (AI), Machine Learning, Asset Management, and Power Transmission System.
Fecha de publicación: junio 2024.
Cita:
Rajora, G.L., Bertling Tjemberg, L., Sanz-Bobi, M.A., On advancements and challenges in asset management for HVDC systems: a machine learning perspective, 18th International Conference on Probabilistic Methods Applied to Power Systems - PMAPS 2024, Auckland (Nueva Zelanda). 24-27 junio 2024.
IIT-24-092C
