A Physics-Grounded Explainable AI Framework for False Data Injection Attack Detection in Smart Grids

Smart grid transmission protection systems are increasingly vulnerable to False Data Injection Attacks (FDIAs), where manipulated relay measurements can compromise protection decisions while remaining difficult to identify using conventional data-driven methods. This paper proposes an explainable machine learning framework that combines reliable FDIA detection with transparent decision interpretation for transmission protection applications. The proposed pipeline employs consensus feature selection by combining Information Gain, Random Forest, and Support Vector Machine rankings, with Correlation based Feature Selection applied as an independent confirmatory check, followed by the best performing Extra Trees configuration identified through cross validated model comparison under a strictly separated training and testing protocol. To improve interpretability, SHAP, LIME, permutation importance, and DiCE counterfactual analysis are jointly incorporated, while a physics consistency audit evaluates whether the dominant relay measurements exhibit electrically plausible behavior. Experiments were conducted on the Mississippi State University and Oak Ridge National Laboratory transmission protection dataset, containing 78,377 labeled observations across fifteen cyber physical scenarios. The final model achieved 93.65% mean accuracy, 0.8848 F1-score, 0.9327 precision, 0.8416 recall, and 0.9820 ROC-AUC, with stable performance across five independent data splits. Unlike conventional black box FDIA detectors, the proposed framework provides global and local explanations together with relay oriented physical validation, enabling operators to understand why an alarm is generated and whether the decision is consistent with underlying electrical behavior. These findings support the development of trustworthy, operationally interpretable protection systems for future smart grids.