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Load frequency control under false data inject attacks based on multi-agent system method in multi-area power systems

Author

Listed:
  • Tengfei Weng
  • Yan Xie
  • Guorong Chen
  • Qi Han
  • Yuan Tian
  • Liping Feng
  • Yangjun Pei

Abstract

This article considers the load frequency control of multi-area power system-based multi-agent system method under false data injection attacks. The research can provide better solutions for multi-area power system load frequency control under false data injection attacks. First, an event-triggered mechanism is introduced to decide which data should be transmitted in the controller to save the limited network bandwidth. Besides, a model of cyberattacks is built using the Bernoulli random variables. Then, conditions are given for maintaining the system asymptotic stability under attack. Finally, simulations are performed to demonstrate the validity of the theory proposed in this article.

Suggested Citation

  • Tengfei Weng & Yan Xie & Guorong Chen & Qi Han & Yuan Tian & Liping Feng & Yangjun Pei, 2022. "Load frequency control under false data inject attacks based on multi-agent system method in multi-area power systems," International Journal of Distributed Sensor Networks, , vol. 18(4), pages 15501329221, April.
    • Handle: RePEc:sae:intdis:v:18:y:2022:i:4:p:15501329221090469
    DOI: 10.1177/15501329221090469
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    References listed on IDEAS

    as
    1. Wang, Dongji & Chen, Fei & Meng, Bo & Hu, Xingliu & Wang, Jing, 2021. "Event-based secure H∞ load frequency control for delayed power systems subject to deception attacks," Applied Mathematics and Computation, Elsevier, vol. 394(C).
    2. Li, Ruoxia & Cao, Jinde, 2016. "Stability analysis of reaction-diffusion uncertain memristive neural networks with time-varying delays and leakage term," Applied Mathematics and Computation, Elsevier, vol. 278(C), pages 54-69.
    3. Li, Jiawen & Yu, Tao & Zhang, Xiaoshun, 2022. "Coordinated load frequency control of multi-area integrated energy system using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 306(PA).
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