Machine Learning Stacking Ensemble for Condition Assessment of Power Cable Networks

Abstract

Underground 15‑kV XLPE cable networks are critical to distribution reliability yet degrade through aging, partial discharge (PD), and corrosion, motivating data‑driven condition assessment beyond periodic manual inspection. This study addresses the need for scalable and explainable models on real utility inspection records by developing an interpretable multi‑model stacking ensemble to classify categorical visual condition states. Using 2,500 asset‑level cable records from Western Canada, four standardized predictors (Age, Partial_Discharge, Neutral_Corrosion, Loading) were analyzed and modeled with Logistic Regression, Gradient Boosting, Random Forest, and a stacking ensemble with a Random Forest meta‑learner. The stacking ensemble achieved the best overall performance, with accuracy 95.80%, precision 95.79%, recall 95.80%, and F1‑score 95.72%, while feature-importance and SHAP-based explanations consistently identified Age and PD as dominant degradation drivers. Future work should validate transferability across multiple utilities, incorporate temporal PD/loading trajectories, quantify uncertainty and drift for deployment, and integrate model outputs into EAM/GIS workflows for operational decision support.