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Deep learning aided interval state prediction for improving cyber security in energy internet

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  • Wang, Huaizhi
  • Ruan, Jiaqi
  • Ma, Zhengwei
  • Zhou, Bin
  • Fu, Xueqian
  • Cao, Guangzhong

Abstract

With the development of advanced information and communication technologies, the electric power grid has been moving forward into an energy internet for improving operational efficiency and reliability. However, energy internet also introduces many internet based entry points, which bring in additional vulnerabilities from malicious cyber-attacks, threatening the economic health of the nations. Therefore, this paper proposes a new defense mechanism based on interval state predictor to effectively detect the malicious attacks. In this mechanism, the variation bounds of each state variable are formulated as a bilevel dual optimization problem. Any resultant state that falls outside the estimated bounds can be recognized as an anomaly, indicating a high possibility of data manipulating. In addition, a typical deep learning algorithm, termed as deep belief network (DBN), is applied for electric load forecasting. DBN has a strong capability for nonlinear feature extraction, which will greatly improve the forecasting accuracy and thus narrow down the variation bounds of state variables, increasing the detection accuracy of the proposed defense mechanism. Finally, the feasibility and effectiveness of the proposed defense mechanism have been validated on IEEE 14- and 118-bus systems.

Suggested Citation

  • Wang, Huaizhi & Ruan, Jiaqi & Ma, Zhengwei & Zhou, Bin & Fu, Xueqian & Cao, Guangzhong, 2019. "Deep learning aided interval state prediction for improving cyber security in energy internet," Energy, Elsevier, vol. 174(C), pages 1292-1304.
    • Handle: RePEc:eee:energy:v:174:y:2019:i:c:p:1292-1304
    DOI: 10.1016/j.energy.2019.03.009
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    Cited by:

    1. Wang, Huaizhi & Liu, Yangyang & Zhou, Bin & Voropai, Nikolai & Cao, Guangzhong & Jia, Youwei & Barakhtenko, Evgeny, 2020. "Advanced adaptive frequency support scheme for DFIG under cyber uncertainty," Renewable Energy, Elsevier, vol. 161(C), pages 98-109.
    2. Tabar, Vahid Sohrabi & Ghassemzadeh, Saeid & Tohidi, Sajjad, 2021. "Increasing resiliency against information vulnerability of renewable resources in the operation of smart multi-area microgrid," Energy, Elsevier, vol. 220(C).
    3. Liang Ma & Gang Xu, 2020. "Distributed Resilient Voltage and Reactive Power Control for Islanded Microgrids under False Data Injection Attacks," Energies, MDPI, vol. 13(15), pages 1-27, July.
    4. Wang, Huaizhi & Meng, Anjian & Liu, Yitao & Fu, Xueqian & Cao, Guangzhong, 2019. "Unscented Kalman Filter based interval state estimation of cyber physical energy system for detection of dynamic attack," Energy, Elsevier, vol. 188(C).
    5. Xie, Yuying & Li, Chaoshun & Tang, Geng & Liu, Fangjie, 2021. "A novel deep interval prediction model with adaptive interval construction strategy and automatic hyperparameter tuning for wind speed forecasting," Energy, Elsevier, vol. 216(C).
    6. Li, Yunfeng & Xue, Wenli & Wu, Ting & Wang, Huaizhi & Zhou, Bin & Aziz, Saddam & He, Yang, 2021. "Intrusion detection of cyber physical energy system based on multivariate ensemble classification," Energy, Elsevier, vol. 218(C).
    7. Zhu, Yunlong & Dong, Zhe & Cheng, Zhonghua & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2023. "Neural network extended state-observer for energy system monitoring," Energy, Elsevier, vol. 263(PA).

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