Correlation-Driven Multi-Level Multimodal Learning for Anomaly Detection on Smart Electric Grid

In multi-energy systems, the continuous expansion of sensor networks and the rapid growth of data dimensions make the traditional single-modality-based anomaly detection strategy difficult to adapt to the challenges of multi-source heterogeneity and potential correlation brought by complex energy consumption scenarios. According to the theory of multimodal fusion algorithms, the Correlation-Driven Multi-Level Multimodal model in this work deeply executes cross-modal collaborative learning and multi-level semantics representation for the data feature of multiple energy sources: Firstly, utilize the multi-channel attention mechanism to mine the correlation between energy consumption data in different modals to realize the implementation of the weight attention of key features. Then, the hierarchical embedding network was used to map the multi-modal features to the high-dimensional unified semantic space, the temporal information and spatial relations were captured through different levels. Moreover, we construct a composite graph relationship which covers multi-source data and the potential interaction in between by combining graph structure learning and adversarial training, to enhance the accuracy and robustness of abnormal features capturing, to realize accurate detection of complex anomalous patterns in multi-energy systems. The experiment results demonstrate that, compared to the previous mainstream methods, the proposed model has a higher detection accuracy and a stronger generalization ability on the multi-source energy datasets.