CABLEGNOSIS Results Presented at EEEIC 2026

CABLEGNOSIS results were presented at the EEEIC 2026 Conference, held in Lisbon, Portugal, in July 2026, showcasing the project’s work on advanced fault diagnosis approaches for modern power systems.

Interview with Nikolay Chavdarov, Head of Power Planning division, Elektroenergien Sistemen Operator EAD (ESO), Bulgaria

We are excited to feature the latest interview in our #MeetTheCABLEGNOSISTeam…

CABLEGNOSIS Project Presented at IFLES 2026 in Beijing

The CABLEGNOSIS project was represented at the International Forum on Large Electric System (IFLES) 2026, held in Beijing, China, from 26 to 28 May 2026, bringing together experts and leading organisations from the international power systems community.

#1: Introducing the Sister Projects on Cables

The CABLEGNOSIS project is pleased to announce the first webinar of its webinar series, bringing together leading European initiatives working on innovative power cable technologies and solutions for the future electricity grid.

#MeetTheCABLEGNOSISTeam Interview Series

Interview with Dr. Richárd Cselkó, Lead Researcher at HUMEA We…

#MeetTheCABLEGNOSIS Team Interview Series

Interview with Markos Asprou, Research Lecturer at KIOS Center…

CABLEGNOSIS at IEEE Conference in UK – Proactive Prognostics for Insulating Materials

As part of the project’s dissemination activities, CABLEGNOSIS researchers (Mohsen Abdolahi, Wenjuan Song, Rob Ross, Aart-Jan Graaf and Mohammad Yazdani-Asrami), received the official acceptance for an abstract in the 15th International Electrical Insulation Conference (INSUCON2026). Authors will participate in-person in INSUCON2026, which will be held in Birmingham, United Kingdom, from 21–23 April 2026.

CABLEGNOSIS Publication in Elsevier – Advanced Condition Monitoring of Power Cables

This study presented an intelligent health-monitoring framework that applies advanced machine learning techniques to assess the condition of 15 kV and 20 kV XLPE cables using features such as partial discharge, age, visual inspection, and neutral corrosion. 18 ML models with Bayesian-optimised hyperparameters were benchmarked, and boosting-based methods achieved the best performance with accuracies above 98%.

CABLEGNOSIS Publication in IEEE –Power Cable Ageing Classification Research

The findings of this paper offers several benefits for real-world cable asset management, as they enable data-driven prioritisation of maintenance and replacement, thereby improving system reliability and cost efficiency.