A reproducible evaluation framework for benchmarking machine learning and hybrid ensemble models in power system anomaly detection
Scientific Reports, Published online: 16 August 2026; doi:10.1038/s41598-026-67295-2 A reproducible evaluation framework for benchmarking machine learning and hybrid ensemble models in power system anomaly detection
Power system anomaly detection faces heightened risks due to digitalization and cyber threats. This paper introduces a reproducible evaluation framework to benchmark machine learning and hybrid ensemble models. Five machine learning models and two hybrid ensemble approaches are compared using voltage, current, load, frequency, power factor, and Total Harmonic Distortion measurements.
Gradient Boosting achieves the highest accuracy of 0.9962, while hybrid models reach accuracies of 0.9989 (Voting Hybrid) and 0.9994 (Stacking Hybrid). The framework incorporates SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) for interpretability. The study's main contribution is providing a unified, reproducible evaluation framework for fair comparison and systematic benchmarking of anomaly detection models in cyber-physical power systems.
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