A Two-Tier Model-Free Defense Approach Against False Data Injection Attacks in Two-Area Load Frequency Control Systems

In open-network environments, two-area load frequency control (LFC) systems are exposed to potential threats from false data injection attacks (FDIAs). Conventional model-based detection and control methods exhibit poor adaptability to unstructured disturbances, making it difficult to ensure system stability and robustness. To address this issue, a two-tier defense mechanism is proposed, in which both the front-end detection and the back-end control components are designed in a model-free manner. The detection module adopts recursive estimation and model-free disturbance observation, while the control module employs a feedback optimal bounded error learning (FOBEL) strategy built on reinforcement learning. The detection module identifies attacks through state residual analysis and signal disturbance estimation, while the control module implements dynamic compensation using a controller that integrates fractional-order structures with reinforcement learning. Compared with traditional methods, this approach demonstrates significant improvements in disturbance rejection and control accuracy. Simulation studies under two representative attack scenarios validate the superiority and effectiveness of the proposed method in terms of frequency deviation suppression, power fluctuation mitigation, and estimation accuracy.