Data-Driven Event-Triggered H ∞ Load Frequency Control With Security Against DoS Attacks

Load frequency control is critical for maintaining grid stability, particularly in modern power systems with wind power penetration and increasing exposure to denial-of-service attacks. This paper presents a data-driven dynamic event-triggered reinforcement learning framework for constrained H∞load frequency control in multi-area power systems. The control problem is formulated as a min–max optimization task, and a dynamic event-triggered strategy is designed to reduce computational and communication burdens. A neural network-based reinforcement learning framework is developed to approximate the near-optimal event-triggered control strategy without requiring explicit system dynamics. To further counteract the impact of frequency-based denial-of-service attacks, a dedicated attacks compensation mechanism is designed. Theoretical analysis proves input-to-state stability of the closed-loop system and guarantees convergence of the neural network parameters. Extensive simulation studies on multi-area power systems with wind power integration demonstrate that the proposed method ensures stable frequency regulation while effectively alleviating the transmission burdens and mitigating the adverse effects of cyberattacks.