Distributed Photovoltaic Communication Anomaly Detection Based on Spatiotemporal Feature Collaborative Modeling

Abstract

As distributed photovoltaic (PV) technology rapidly develops and is widely applied, the methods of cyberattacks are continuously evolving, posing increasingly severe threats to the communication networks of distributed PV systems. Recent studies have shown that the Transformer model, which effectively integrates global information and handles long-distance dependencies, has garnered significant attention. Based on this, our research proposes a model named STformer, which is applied to the task of attack detection in distributed PV communication. Specifically, we propose a temporal attention mechanism and a variable attention mechanism. The temporal attention mechanism focuses on capturing subtle changes and trends in data sequences over time, ensuring a highly sensitive recognition of patterns inherent in time-series data. In contrast, the variable attention mechanism analyzes the intrinsic relationships and interactions between different variables, uncovering critical correlations that may indicate abnormal behavior or potential attacks. Additionally, we incorporate the Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction technique. This technique not only helps reduce computational complexity but, in certain cases, can enhance anomaly detection performance. Finally, compared to classical and advanced methods, STformer demonstrates satisfactory performance in simulation experiments.

Keywords

• photovoltaic microgrid
• deep learning
• cyberattack detection
• attention mechanism