A Novel Health Index for MV Cable Joint Aging Prediction Based on Dynamic Graph Attention Model

The distribution network relies on a critical electric component, the cable joint (CJ), for expansion and post-fault repairs. In some parts of the network, a CJ can be found every few 100 m, making it a crucial element that requires thorough study. Additionally, the CJ is highly vulnerable, and its failure often results in explosions following a fault in a problematic network branch. Unfortunately, the exact relationship between CJ parameters and their health index (HI) is unknown. This work aims to enhance existing knowledge by proposing a new and flexible HI and using a machine learning-based approach to estimate CJ aging. The proposed HI considers numerous factors, detailed in the text, and is adaptable to the information available to system operators (SOs). By means of a public dataset with condition monitoring data, the machine learning algorithm is validated to demonstrate its efficacy in estimating the HI and forecasting future CJ parameters.