Detecting False Data Injection and Unstable Operation in Smart Grid via System-Aware Graph Boundary Learning

2026-09-28

AI Summary

This study addresses the scarcity of instability samples and the challenge of jointly detecting them with false data injection (FDI) attacks in smart grids by proposing the StarGNN framework. Based on producer-consumer star graph modeling and a role-aware graph neural network, this method learns security boundaries using exclusively stable operational data. By incorporating physics-informed constraints to generate pseudo-negative samples, it enables simultaneous identification of genuine instabilities and stealthy FDI attacks via a single threshold without requiring anomaly labels. Experimental results demonstrate that across nine previously unseen FDI scenarios, the system achieves attack detection rates of 0.78–0.97 and instability recall exceeding 0.97. This work effectively bridges the technical gap between stability prediction and attack detection while exhibiting robustness against adaptive adversarial strategies.

Abstract

Cyber-physical power systems increasingly rely on data-driven tools to detect instability and support reliable grid operation. However, reliable stability prediction is difficult when unstable operating configurations are rare, sensitive, or unavailable during model development, since collecting such data safely and at scale is often impractical. At the same time, False Data Injection (FDI) attacks can manipulate reported system parameters to trigger false instability alarms or conceal unsafe operation, and such threats in Decentral Smart Grid Control (DSGC) systems remain largely unexplored. These challenges are rarely addressed jointly, leaving a gap between stability prediction and attack detection that this work aims to close. In this paper, we introduce StarGNN, a graph learning framework that learns the stable operating region exclusively from clean stable configurations and uses a single abnormality score to flag reported configurations that should not be trusted as evidence of safe operation, covering both genuine instability and unseen FDI manipulations. Each configuration is represented as a producer-consumer star graph and processed by a role aware graph neural network, with physics constrained pseudo-negatives generated by perturbing reaction time and price response parameters standing in for the unavailable unstable and attack data. A single threshold, calibrated only on held-out stable data, is used without task or attack specific adjustment. Evaluated on nine unseen FDI scenarios, StarGNN detects between 0.780 and 0.973 of attacks on stable configurations and retains a post attack instability recall between 0.972 and 0.999 on unstable ones, with 0.899 recall against a stronger adaptive attacker, showing that stable-only boundary learning can support both stability prediction and attack detection without access to genuine unstable labels or attack samples during training.

Problem

• False Data Injection
• Smart Grid Stability
• Cyber-Physical Power Systems
• Decentralized Smart Grid Control
• Anomaly Detection

Innovation

• Graph Neural Network
• False Data Injection
• Boundary Learning
• Pseudo-negatives
• Smart Grid