Data-Driven Event-Triggered H∞ Load Frequency Control with Security Against DoS Attacks
Authors: Chen, Y., Zhao, H., Ma, L., Yang, Q., Yu, H., Peng, L.
Journal: IEEE Transactions on Automation Science and Engineering
Publication Date: 01/01/2026
Volume: 23
Pages: 4602-4614
eISSN: 1558-3783
ISSN: 1545-5955
DOI: 10.1109/TASE.2026.3660419
Abstract: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. Note to Practitioners - 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 ∞ 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 attacks 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.
Source: Scopus