IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v174y2019icp1292-1304.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054421930413X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2019.03.009?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wang, H.Z. & Wang, G.B. & Li, G.Q. & Peng, J.C. & Liu, Y.T., 2016. "Deep belief network based deterministic and probabilistic wind speed forecasting approach," Applied Energy, Elsevier, vol. 182(C), pages 80-93.
    2. Hua, Haochen & Qin, Yuchao & Hao, Chuantong & Cao, Junwei, 2019. "Optimal energy management strategies for energy Internet via deep reinforcement learning approach," Applied Energy, Elsevier, vol. 239(C), pages 598-609.
    3. Hong, Bowen & Zhang, Weitong & Zhou, Yue & Chen, Jian & Xiang, Yue & Mu, Yunfei, 2018. "Energy-Internet-oriented microgrid energy management system architecture and its application in China," Applied Energy, Elsevier, vol. 228(C), pages 2153-2164.
    4. Zhou, Xiaoyong & Zhou, Dequn & Wang, Qunwei, 2018. "How does information and communication technology affect China's energy intensity? A three-tier structural decomposition analysis," Energy, Elsevier, vol. 151(C), pages 748-759.
    5. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda & Song, Jiakang, 2018. "Deep belief network based k-means cluster approach for short-term wind power forecasting," Energy, Elsevier, vol. 165(PA), pages 840-852.
    6. Månsson, André & Johansson, Bengt & Nilsson, Lars J., 2014. "Assessing energy security: An overview of commonly used methodologies," Energy, Elsevier, vol. 73(C), pages 1-14.
    7. Zhang, Jingrui & Wang, Silu & Tang, Qinghui & Zhou, Yulu & Zeng, Tao, 2019. "An improved NSGA-III integrating adaptive elimination strategy to solution of many-objective optimal power flow problems," Energy, Elsevier, vol. 172(C), pages 945-957.
    8. Mahmud, Khizir & Town, Graham E. & Morsalin, Sayidul & Hossain, M.J., 2018. "Integration of electric vehicles and management in the internet of energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 4179-4203.
    9. Si, Fangyuan & Wang, Jinkuan & Han, Yinghua & Zhao, Qiang & Han, Peng & Li, Yan, 2018. "Cost-efficient multi-energy management with flexible complementarity strategy for energy internet," Applied Energy, Elsevier, vol. 231(C), pages 803-815.
    10. Khemakhem, Siwar & Rekik, Mouna & Krichen, Lotfi, 2019. "Double layer home energy supervision strategies based on demand response and plug-in electric vehicle control for flattening power load curves in a smart grid," Energy, Elsevier, vol. 167(C), pages 312-324.
    11. Basetti, Vedik & Chandel, Ashwani K. & Chandel, Rajeevan, 2016. "Power system dynamic state estimation using prediction based evolutionary technique," Energy, Elsevier, vol. 107(C), pages 29-47.
    12. Zou, Changfu & Hu, Xiaosong & Wei, Zhongbao & Tang, Xiaolin, 2017. "Electrothermal dynamics-conscious lithium-ion battery cell-level charging management via state-monitored predictive control," Energy, Elsevier, vol. 141(C), pages 250-259.
    13. Zhang, Jie & Cui, Mingjian & Hodge, Bri-Mathias & Florita, Anthony & Freedman, Jeffrey, 2017. "Ramp forecasting performance from improved short-term wind power forecasting over multiple spatial and temporal scales," Energy, Elsevier, vol. 122(C), pages 528-541.
    14. Bartolucci, Lorenzo & Cordiner, Stefano & Mulone, Vincenzo & Rocco, Vittorio & Rossi, Joao Luis, 2018. "Hybrid renewable energy systems for renewable integration in microgrids: Influence of sizing on performance," Energy, Elsevier, vol. 152(C), pages 744-758.
    15. Hyysalo, Sampsa & Juntunen, Jouni K. & Martiskainen, Mari, 2018. "Energy Internet forums as acceleration phase transition intermediaries," Research Policy, Elsevier, vol. 47(5), pages 872-885.
    16. Luo, Yugong & Zhu, Tao & Wan, Shuang & Zhang, Shuwei & Li, Keqiang, 2016. "Optimal charging scheduling for large-scale EV (electric vehicle) deployment based on the interaction of the smart-grid and intelligent-transport systems," Energy, Elsevier, vol. 97(C), pages 359-368.
    17. Reka, S. Sofana & Dragicevic, Tomislav, 2018. "Future effectual role of energy delivery: A comprehensive review of Internet of Things and smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 90-108.
    18. Zhou, Kaile & Yang, Shanlin & Shao, Zhen, 2016. "Energy Internet: The business perspective," Applied Energy, Elsevier, vol. 178(C), pages 212-222.
    19. Good, Clara & Shepero, Mahmoud & Munkhammar, Joakim & Boström, Tobias, 2019. "Scenario-based modelling of the potential for solar energy charging of electric vehicles in two Scandinavian cities," Energy, Elsevier, vol. 168(C), pages 111-125.
    20. Osorio, Sebastian & van Ackere, Ann & Larsen, Erik R., 2017. "Interdependencies in security of electricity supply," Energy, Elsevier, vol. 135(C), pages 598-609.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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).
    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Akhil Joseph & Patil Balachandra, 2020. "Energy Internet, the Future Electricity System: Overview, Concept, Model Structure, and Mechanism," Energies, MDPI, vol. 13(16), pages 1-26, August.
    2. Mir Hamid Taghavi & Peyman Akhavan & Rouhollah Ahmadi & Ali Bonyadi Naeini, 2022. "Identifying Key Components in Implementation of Internet of Energy (IoE) in Iran with a Combined Approach of Meta-Synthesis and Structural Analysis: A Systematic Review," Sustainability, MDPI, vol. 14(20), pages 1-23, October.
    3. Ahmad, Tanveer & Chen, Huanxin, 2019. "Deep learning for multi-scale smart energy forecasting," Energy, Elsevier, vol. 175(C), pages 98-112.
    4. Mauricio de Castro Tomé & Pedro H. J. Nardelli & Hafiz Majid Hussain & Sohail Wahid & Arun Narayanan, 2020. "A Cyber-Physical Residential Energy Management System via Virtualized Packets," Energies, MDPI, vol. 13(3), pages 1-18, February.
    5. Wu, Ying & Wu, Yanpeng & Guerrero, Josep M. & Vasquez, Juan C., 2021. "A comprehensive overview of framework for developing sustainable energy internet: From things-based energy network to services-based management system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    6. Evgeny Lisin & Wadim Strielkowski & Veronika Chernova & Alena Fomina, 2018. "Assessment of the Territorial Energy Security in the Context of Energy Systems Integration," Energies, MDPI, vol. 11(12), pages 1-14, November.
    7. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    8. Zhang, Yu & Li, Yanting & Zhang, Guangyao, 2020. "Short-term wind power forecasting approach based on Seq2Seq model using NWP data," Energy, Elsevier, vol. 213(C).
    9. Haibo Chu & Jiahua Wei & Yuan Jiang, 2021. "Middle- and Long-Term Streamflow Forecasting and Uncertainty Analysis Using Lasso-DBN-Bootstrap Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(8), pages 2617-2632, June.
    10. Ghimire, Sujan & Deo, Ravinesh C. & Raj, Nawin & Mi, Jianchun, 2019. "Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    11. Yang, Xiaohui & Chen, Zaixing & Huang, Xin & Li, Ruixin & Xu, Shaoping & Yang, Chunsheng, 2021. "Robust capacity optimization methods for integrated energy systems considering demand response and thermal comfort," Energy, Elsevier, vol. 221(C).
    12. Nathan Oaks Farrar & Mohd Hasan Ali & Dipankar Dasgupta, 2023. "Artificial Intelligence and Machine Learning in Grid Connected Wind Turbine Control Systems: A Comprehensive Review," Energies, MDPI, vol. 16(3), pages 1-25, February.
    13. Bartolucci, Lorenzo & Cordiner, Stefano & Mulone, Vincenzo & Santarelli, Marina, 2019. "Hybrid renewable energy systems: Influence of short term forecasting on model predictive control performance," Energy, Elsevier, vol. 172(C), pages 997-1004.
    14. Florentina Magda Enescu & Nicu Bizon & Adrian Onu & Maria Simona Răboacă & Phatiphat Thounthong & Alin Gheorghita Mazare & Gheorghe Șerban, 2020. "Implementing Blockchain Technology in Irrigation Systems That Integrate Photovoltaic Energy Generation Systems," Sustainability, MDPI, vol. 12(4), pages 1-30, February.
    15. Zucatelli, P.J. & Nascimento, E.G.S. & Santos, A.Á.B. & Arce, A.M.G. & Moreira, D.M., 2021. "An investigation on deep learning and wavelet transform to nowcast wind power and wind power ramp: A case study in Brazil and Uruguay," Energy, Elsevier, vol. 230(C).
    16. Ramya Kuppusamy & Srete Nikolovski & Yuvaraja Teekaraman, 2023. "Review of Machine Learning Techniques for Power Quality Performance Evaluation in Grid-Connected Systems," Sustainability, MDPI, vol. 15(20), pages 1-29, October.
    17. Sharifzadeh, Mahdi & Sikinioti-Lock, Alexandra & Shah, Nilay, 2019. "Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 513-538.
    18. Liu, Hui & Yu, Chengqing & Wu, Haiping & Duan, Zhu & Yan, Guangxi, 2020. "A new hybrid ensemble deep reinforcement learning model for wind speed short term forecasting," Energy, Elsevier, vol. 202(C).
    19. József Magyari & Krisztina Hegedüs & Botond Sinóros-Szabó, 2022. "Integration Opportunities of Power-to-Gas and Internet-of-Things Technical Advancements: A Systematic Literature Review," Energies, MDPI, vol. 15(19), pages 1-19, September.
    20. 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).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:174:y:2019:i:c:p:1292-1304. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.