Explainable machine learning for health assessment of PILC distribution cables

Abstract

While Paper Insulated Lead Covered (PILC) cables are a legacy technology, they remain a critical fixture in medium-voltage power grids. However, as these cables are continuously used, they present growing risks to grid stability and public safety due to electrical, thermal, and environmental factors. As replacing entire networks is cost-prohibitive, utility providers need a smarter way to predict when and where a cable might fail. This paper presents an explainable, data-driven framework for multi-class health assessment of 20 kV PILC cables, aligning with the industry 4.0 standards of autonomous asset management. Using a real-world dataset of 999 inspection records from European utilities, four supervised learning models; Random Forest, AdaBoost, XGBoost, and a Multilayer Perceptron deep neural network were trained. The models categorize cable health into five standard IEC/IEEE health bands, achieving high classification accuracies between 97.0% and 99.5%. To ensure transparency in the decision-making process, SHapley Additive exPlanations (SHAP) were employed, identifying Partial Discharge (PD) and Thermal Difference (TD) Stability as the primary predictors of insulation degradation. This framework provides an interpretable tool for power utilities to transition from reactive to predictive maintenance scheduling.