Abstract: 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.

Layth Almahadeen, Aseel Smerat, Sandeep Kumar Mathariya, G. Indra Navaroj, Vuda Sreenivasa Rao, Kamila Ibragimova and Osama R.Shahin. “Meta Learning Enhanced Graph Transformer for Robust Smart Grid Anomaly Detection”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.11 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161171

@article{Almahadeen2025,
title = {Meta Learning Enhanced Graph Transformer for Robust Smart Grid Anomaly Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161171},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161171},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {11},
author = {Layth Almahadeen and Aseel Smerat and Sandeep Kumar Mathariya and G. Indra Navaroj and Vuda Sreenivasa Rao and Kamila Ibragimova and Osama R.Shahin}
}