Comparative Analysis of Supervised Machine Learning Anomaly Classification on Active Power in Smart Grids.

The primary contributions of this study are as follows. First, it presents a focused investigation on active power–based anomaly classification in smart grid physical infrastructure by utilizing the physical attack subset of a cyber–physical dataset. Second, it conducts a systematic comparative evaluation of multiple supervised classifiers under physical attack scenarios, employing rigorous multi-class performance metrics such as precision, recall, F1-score, and confusion matrices. Third, it applies Random Forest–based feature-importance methods, including Gini impurity and permutation importance, to identify influential features, reduce dimensionality, and enhance interpretability. Finally, the study advances toward an explainability-driven and practically deployable anomaly classification and detection framework for smart grid monitoring, addressing limitations of prior works constrained by dataset scope, conceptual focus, or narrow evaluation metrics.