Research on Detection and Defense Methods for False Data Injection Attacks in Power Systems Based on State-Space Decomposition

With increasing renewable energy integration, load frequency control (LFC) faces security risks from false data injection attacks (FDIAs). Existing detection methods struggle to distinguish control input attacks from measurement attacks, affecting system stability. This paper develops a state-space model for LFC incorporating renewable energy and energy storage systems (ESS) and analyzes FDIA impacts on system dynamics. A state-space decomposition method is used to decouple attack signals, improving detection accuracy. A sliding mode observer (SMO)-based attack estimation (AE) method is proposed for real-time detection. Additionally, an attack-resilient control (ARC) strategy is designed using H _∞ control theory to enhance robustness. Simulations show that the proposed method reduces AE mean squared error by nearly 30% and improves frequency response stability. These results confirm its effectiveness in detecting FDIAs and enhancing power system security.