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Load altering attack-tolerant defense strategy for load frequency control system

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  • Chen, Chunyu
  • Cui, Mingjian
  • Fang, Xin
  • Ren, Bixing
  • Chen, Yang

Abstract

Cyber attacks become emerging threats to every information-oriented energy management system. By violating the cyber systems, the hacker can disrupt the security and stability due to the strong coupling between the cyber and physical facilities. In this paper, one type of cyber attacks designated as the load altering attack is studied for the power system frequency control, and corresponding defense strategies are proposed to improve the frequency control performance. Considering the difficulty of the application of model-based controller into large-scale power systems, a novel model-free defense framework is for the first time presented. Under this framework, both active defense and passive defense strategies are designed. The former assumes that the defender has the initiative to learn different attack scenarios. Adaptive defense strategies are implemented using the online attack identification information and off-line trained strategy pool. The latter assumes that the defender passively tolerates various attack scenarios via the pre-trained off-line strategy. Both approaches prove to be effective through validation based on the IEEE benchmark systems. The proposed defense framework and defense strategies can be extended to other energy control systems to enhance their attack tolerance capability.

Suggested Citation

  • Chen, Chunyu & Cui, Mingjian & Fang, Xin & Ren, Bixing & Chen, Yang, 2020. "Load altering attack-tolerant defense strategy for load frequency control system," Applied Energy, Elsevier, vol. 280(C).
    • Handle: RePEc:eee:appene:v:280:y:2020:i:c:s0306261920314586
    DOI: 10.1016/j.apenergy.2020.116015
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    References listed on IDEAS

    as
    1. Saad, Ahmed A. & Faddel, Samy & Mohammed, Osama, 2019. "A secured distributed control system for future interconnected smart grids," Applied Energy, Elsevier, vol. 243(C), pages 57-70.
    2. Shahid, Farah & Zameer, Aneela & Mehmood, Ammara & Raja, Muhammad Asif Zahoor, 2020. "A novel wavenets long short term memory paradigm for wind power prediction," Applied Energy, Elsevier, vol. 269(C).
    3. Wu, Peng & Partridge, Julius & Bucknall, Richard, 2020. "Cost-effective reinforcement learning energy management for plug-in hybrid fuel cell and battery ships," Applied Energy, Elsevier, vol. 275(C).
    4. Sayed, Ahmed R. & Wang, Cheng & Bi, Tianshu, 2019. "Resilient operational strategies for power systems considering the interactions with natural gas systems," Applied Energy, Elsevier, vol. 241(C), pages 548-566.
    5. Luo, Xiaoyuan & Wang, Xinyu & Zhang, Mingyue & Guan, Xinping, 2019. "Distributed detection and isolation of bias injection attack in smart energy grid via interval observer," Applied Energy, Elsevier, vol. 256(C).
    6. Zhang, Wei & Wang, Jixin & Liu, Yong & Gao, Guangzong & Liang, Siwen & Ma, Hongfeng, 2020. "Reinforcement learning-based intelligent energy management architecture for hybrid construction machinery," Applied Energy, Elsevier, vol. 275(C).
    7. Lai, Kexing & Illindala, Mahesh & Subramaniam, Karthikeyan, 2019. "A tri-level optimization model to mitigate coordinated attacks on electric power systems in a cyber-physical environment," Applied Energy, Elsevier, vol. 235(C), pages 204-218.
    8. Mishra, Sakshi & Anderson, Kate & Miller, Brian & Boyer, Kyle & Warren, Adam, 2020. "Microgrid resilience: A holistic approach for assessing threats, identifying vulnerabilities, and designing corresponding mitigation strategies," Applied Energy, Elsevier, vol. 264(C).
    9. Shang-Guan, Xingchen & He, Yong & Zhang, Chuanke & Jiang, Lin & Spencer, Joseph William & Wu, Min, 2020. "Sampled-data based discrete and fast load frequency control for power systems with wind power," Applied Energy, Elsevier, vol. 259(C).
    10. Latif, Abdul & Hussain, S.M. Suhail & Das, Dulal Chandra & Ustun, Taha Selim, 2020. "State-of-the-art of controllers and soft computing techniques for regulated load frequency management of single/multi-area traditional and renewable energy based power systems," Applied Energy, Elsevier, vol. 266(C).
    11. Li, Yuecheng & He, Hongwen & Khajepour, Amir & Wang, Hong & Peng, Jiankun, 2019. "Energy management for a power-split hybrid electric bus via deep reinforcement learning with terrain information," Applied Energy, Elsevier, vol. 255(C).
    Full references (including those not matched with items on IDEAS)

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    Cited by:

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    2. Yin, Hao & Ou, Zuhong & Fu, Jiajin & Cai, Yongfeng & Chen, Shun & Meng, Anbo, 2021. "A novel transfer learning approach for wind power prediction based on a serio-parallel deep learning architecture," Energy, Elsevier, vol. 234(C).
    3. Jianguo Ding & Attia Qammar & Zhimin Zhang & Ahmad Karim & Huansheng Ning, 2022. "Cyber Threats to Smart Grids: Review, Taxonomy, Potential Solutions, and Future Directions," Energies, MDPI, vol. 15(18), pages 1-37, September.
    4. Désiré D. Rasolomampionona & Michał Połecki & Krzysztof Zagrajek & Wiktor Wróblewski & Marcin Januszewski, 2024. "A Comprehensive Review of Load Frequency Control Technologies," Energies, MDPI, vol. 17(12), pages 1-77, June.
    5. Ding, Zhetong & Chen, Chunyu & Cui, Mingjian & Bi, Wenjun & Chen, Yang & Li, Fangxing, 2021. "Dynamic game-based defensive primary frequency control system considering intelligent attackers," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    6. Shangguan, Xing-Chen & He, Yong & Zhang, Chuan-Ke & Jiang, Lin & Wu, Min, 2022. "Load frequency control of time-delayed power system based on event-triggered communication scheme," Applied Energy, Elsevier, vol. 308(C).

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