Data-Driven Event-Triggered H-infty Load Frequency Control with Security Against DoS Attacks

The reliable operation of multi-area interconnected power systems is becoming increasingly difficult due to two practical challenges: the rapid frequency fluctuations introduced by renewable energy, and the growing risk of cyberattacks that disrupt communication links. To handle these practical challenges, this work develops a data-driven dynamic event-triggered reinforcement learning framework for constrained H$_\\infty$ load frequency control in multi-area power systems. The approach avoids reliance on precise system models, making it suitable for complex and uncertain environments. The event-triggered mechanism effectively reduces communication and computation burdens, which only updates control signals when necessary, thus being suitable for large-scale interconnected grids. Moreover, an attack compensation mechanism is designed to enhance resilience against denial-of-service attacks. Simulation results demonstrate that the method improves both reliability and efficiency compared with existing solutions. The practical limitation of this approach lies in its reliance on reinforcement learning, which means it requires a period of learning to achieve optimal control performance. These results provide practical insights for implementing robust and data-driven load frequency control strategies in multi-area interconnected power systems. Beyond application in power systems, the proposed ideas may also be extended to other automation systems, where communication security and resource efficiency are critical.