A KAN-SHAP Framework for Fault Detection and Analysis in Smart Grids

Predictive maintenance is critical for ensuring the reliability and efficiency of Medium Voltage (MV) power grids. This paper presents a novel framework combining Kolmogorov–Arnold Networks (KANs) with SHapley Additive ex-Planations (SHAP) to predict and interpret real-world faults detected in Azienda Comunale Energia e Ambiente (ACEA)’s MV grid in Rome (Italy). The KAN model captures complex nonlinear interactions between Constitutive Parameters (CPs) and Exogenous Causes (ECs) measured through smart sensors, achieving high predictive performance with a ROC AUC of 0.993 over several traditional classifiers. SHAP analysis enhances interpretability, revealing ECs, such as mean and maximum currents, as dominant predictors, while CPs, including cable length and material composition, highlight structural vulnerabilities. Insights from clustering and dependence analyses enable targeted maintenance strategies by distinguishing fault scenarios driven by environmental or structural factors. This integrative approach bridges predictive accuracy and actionable interpretability – within the explainable AI (XAI) paradigm, providing a robust tool for smart grid management and condition-based maintenance.