Meta Learning Enhanced Graph Transformer for Robust Smart Grid Anomaly Detection

The increasing complexity of modern smart grids and the heterogeneity of multi-sensor data make anomaly detection extremely challenging, as existing techniques struggle to capture long-range spatial dependencies, cross-sensor interactions, and unseen anomaly patterns. Conventional models such as Isolation Forest, Random Forest, GCAD, AT-GTL, CVTGAD, and hybrid CNN-Transformer approaches often suffer from limited generalization, weak multimodal fusion, and strong dependence on labeled anomalies. To address these limitations, this study introduces a novel Multimodal Graph Transformer with Contrastive Self-Supervised Learning and Model-Agnostic Meta-Learning (MGT-CGSSML), a uniquely integrated framework designed to learn structural, attribute, and cross-modal relationships simultaneously. The proposed method stands out by combining multimodal graph encoding, dual-view contrastive learning, and fast meta-adaptation, enabling the model to rapidly identify new anomaly types with minimal labeled data. Implemented in Python using PyTorch, the model is evaluated on a multimodal smart grid dataset containing time-stamped voltage, current, power factor, frequency, temperature, and humidity measurements recorded at 15-minute intervals. Experimental results demonstrate 96.5% accuracy, 95% precision, 95.5% recall, and 95.2% F1-score, reflecting a 3–5% performance improvement over advanced baseline models due to enhanced multimodal fusion and meta-learning optimization. The study concludes that MGT-CGSSML delivers a scalable, interpretable, and real-time anomaly detection solution capable of supporting resilient and adaptive smart-grid operations, offering substantial advancements over existing methods.

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