Anomaly Detection in Smart Power Grids with Graph-Regularized MS-SVDD: a Multimodal Subspace Learning Approach

TL;DR

This paper introduces a graph-regularized multimodal subspace learning model for anomaly detection in smart power grids, enhancing robustness by embedding structural dependencies across sensor data modalities.

Contribution

It proposes a novel MS-SVDD model with graph embedding regularization that better exploits inter-modality relationships for improved anomaly detection.

Findings

Graph-regularized MS-SVDD outperforms traditional methods in detecting anomalies.

The approach effectively preserves modality-specific structures in a shared subspace.

Results demonstrate increased robustness in real-world smart grid scenarios.

Abstract

Anomaly detection in smart power grids is a critical challenge due to the complexity, heterogeneity, and dynamic nature of sensor data streams. Existing one-class classification methods, particularly Subspace Support Vector Data Description (SVDD), have been extended to multimodal scenarios but often fail to fully exploit the structural dependencies across modalities, limiting their robustness in real-world applications. In this paper, we address this gap by proposing a generalized Multimodal Subspace Support Vector Data Description (MS-SVDD) model with graph-embedded regularization. The method projects data from multiple modalities into a shared low-dimensional subspace while preserving modality-specific structure through Laplacian regularizers. Our approach is evaluated on a three-modality dataset derived from smart grid event time series, using a dedicated preprocessing pipeline for constructing one-class classification training samples. The results demonstrate that our graph-embedded MS-SVDD improves robustness of event detection compared to conventional approaches, highlighting the potential of integrating graph priors with multimodal subspace learning for advancing anomaly detection in critical infrastructure. More broadly, this work contributes to the wider field of AI by illustrating how relational and structural information can be systematically embedded into one-class models, enabling robust learning under complex, high-dimensional, and multimodal conditions.