Research on detection and defense methods for false data injection attacks in power systems based on state-space decomposition - Chao Hong; Zhihong Liang; Yiwei Yang; Pandeng Li; Lin Chen; Leyi Bi; Yunan Zhang (Discover Applied Sciences)
Abstract 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 formulates a novel state-space decomposition strategy for decoupling control input and measurement attacks in the context of LFC systems with high renewable penetration; introduces a sliding mode observer (SMO) tailored for real-time attack estimation; and integrates an attack-resilient control (ARC) scheme based on $$H_∞$$ H ∞ control theory to actively suppress attack impacts. 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.