Skip to main content
SpringerLink
Log in

Risk Assessment of Electric Energy Metering Automation System Based on Bayesian Network

  • Conference paper
  • First Online:
Artificial Intelligence and Security (ICAIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13340))

Included in the following conference series:

  • 1087 Accesses

Abstract

In the ubiquitous network environment, the security threats facing the metering automation system are also increasing. The risk assessment of electric energy measurement automation system is an important goal of power grid security, but it faces difficulties such as single hidden danger identification means and lack of dynamic assessment models. To improve the accuracy and rationality of the system risk assessment, this paper realizes the dynamic risk assessment of the electric energy metering automation system. First, a risk assessment index system is established from the three aspects of technology, management, and regulations. Secondly, based on analytic hierarchy process, we analyzed the weight of the risk assessment indicator to obtain the subjective weight of the risk assessment indicator. Then, the Bayes grid method is improved by the method of probability distribution, which quantitatively describe the relationship between parent nodes and child nodes. Through the improved Bayesian grid method, the objective weight of the risk assessment indicator is obtained, and the comprehensive weight of the assessment indicator is calculated through combination weighting, which realizes the comprehensive risk assessment of the measurement automation system. Finally, the simulation experiment analysis and the sensitivity analysis of the proposed model are carried out. The result shows that the safety guarantee goal of the electric energy metering automation system depends to a large extent on the technical reliability.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Jin, L., Mesiar, R., Qian, G.: Weighting models to generate weights and capacities in multicriteria group decision making. IEEE Trans. Fuzzy Syst. 26(4), 2225–2236 (2018)

    Article  Google Scholar 

  2. Wang, L., Chen, Y., Yang, M., Yang, S., Zhang, H.: Research on deepening application of measurement operation data and intelligent fault recognition based on cloud platform. Electr. Measure. Instrument. 57(7), 87–92 (2020)

    Google Scholar 

  3. Dong, X., Qiao, K.: Research for orient mode and key technology of China’s smart gird. J. Electric Power 25(4), 287–291 (2010)

    MathSciNet  Google Scholar 

  4. Zhang, F.: Risk-based information security management system. Netw. Secur. Technol. Appl. 3, 5–6 (2019)

    Google Scholar 

  5. Ma, H.: The construction and application of a dynamic assessment model of information security risks for electric power enterprises. Electr. Power Inform. Commun. Technol. 15, 22–25 (2017)

    Google Scholar 

  6. Santos, R., Marinho, R., Schmitt, G.R.: A framework and risk assessment approaches for risk-based access control in the cloud. J. Netw. Comput. Appl. 74, 86–97 (2016)

    Article  Google Scholar 

  7. Ding, G., Fan, B., Lan, H., Long, T., Wang, J.: Research on smart grid information security threats and defense strategies. Electr. Power Inform. Commun. Technol. 12(5), 58–63 (2014)

    Google Scholar 

  8. Cao, J., Yang, M., Zhang, D., Ming, Y.: Energy internet-information and energy infrastructure integration. South. Power Grid Technol. 8(4), 1–10 (2014)

    Google Scholar 

  9. Yang, S., Tang, B., Yao, J., Li, F.: Automatic intelligent dispatching architecture and key technologies of power grid based on situation awareness. Power Syst. Technol. 38(1), 33–39 (2014)

    Google Scholar 

  10. Sun, Y., Wu, J., Li, G.: Dynamic economic dispatch of power system containing wind farm based on wind speed forecast and stochastic programming. Proc. Chin. Soc. Electr. Eng. 29(4), 41–47 (2009)

    Google Scholar 

  11. Cheng, Z.: Key technologies for optimal dispatching of intelligent distribution networks. Electron Technol. 49, 186–188 (2020)

    Google Scholar 

  12. Mccalley, J.D., Vittal, V., Abi-Samra, N.: An overview of risk based security assessment. Power Engineering Society (1999)

    Google Scholar 

  13. Kirschen, S., Jayaweera, D.: Comparison of risk-based and deterministic security assessments. Gener. Trans. Distrib. 1, 527–533 (2007)

    Article  Google Scholar 

  14. Mccalley, D., Fouad, A.: A risk-based security indicator for determining operating limits in stability-limited electric power systems. IEEE Trans. Power Syst. 12(3), 1210–1219 (1997)

    Article  Google Scholar 

  15. Ni, M., James, D.: Online risk-based security assessment. IEEE Trans. Power Syst. 18(1), 258–265 (2003)

    Google Scholar 

  16. Hu, S., Chao, Z., Zhong, H., Xie, M., Luo, W., Liu, M.: Modeling and application of risk consequence value of power grid dispatching operation. Autom. Electr. Power Syst. 40(7), 54–60 (2016)

    Article  Google Scholar 

  17. Chen, W., Jiang, Q., Cao, Y., Han, Z.: Risk assessment of power system voltage collapse. Power Syst. Technol. 19, 36–41 (2005)

    Google Scholar 

  18. Shi, H., et al.: On-line assessment of transmission network operation risk. Power Syst. Technol. 6, 43–48 (2005)

    Google Scholar 

  19. Yong, J., Mccalley, D., Voorhis, V.: Risk-based resource optimization for transmission system maintenance. IEEE Trans. Power Syst. 21(3), 1191–1200 (2006)

    Google Scholar 

  20. Jeyaraj, P.R., Kathiresan, A.C., Asokan, S.P., Rajan, E., Rezk, H.: Power system resiliency and wide area control employing deep learning algorithm. Comput. Mater. Continua 68(1), 553–567 (2021)

    Article  Google Scholar 

  21. Tang, B., Zhang, S., Tang, F., Jun, H.L., Zou, X.Z.: Research and analysis on the annual failure rate of transmission line ice damage. Electr. Measur. Instrument. 57(17), 29–33 (2020)

    Google Scholar 

  22. Li, Q., Yang, X., Liu, Z., Li, Z., Zhao, S., He, X.: Gray cluster analysis-based transmission line galloping classification early warning scheme. Electr. Measure. Instrument. 57(17), 45–51 (2020)

    Google Scholar 

  23. Huang, J., Hua-Qiang, Z.Y.: Power grid security risk assessment based on comprehensive element influence indicator. Sci. Technol. Eng. 17(23), 201–208 (2017)

    Google Scholar 

  24. Dong, M., Nassif, A., Li, B.: A data-driven residential transformer overloading risk assessment method. IEEE Trans. Power Delivery 34, 387–396 (2019)

    Article  Google Scholar 

  25. Shi, X., Qiu, R., Mi, T., He, X., Zhu, Y.: Adversarial feature learning of online monitoring data for operational risk assessment in distribution networks. IEEE Trans. Power Syst. 35(2), 975–985 (2019)

    Article  Google Scholar 

  26. Chen, H., Liu, P., Wu, G., Kuang, B.: Analysis of the impact of low-frequency power grid faults on the operational safety of nuclear power plants. In: Thermal Power Generation, pp. 1–8 (2015)

    Google Scholar 

  27. Jiang, Y., Wu, Y., Luan, Y., Zhou, B., Zhao, Z., Zhou, H.: The upgraded functions and applications of the comprehensive load forecasting platform of Yunnan power grid. Yunnan Electr. Power Technol. 46(6), 44–47 (2018)

    Google Scholar 

  28. Ma, X., Zhang, L.: Interpretation of gao’s ‘protection of critical infrastructure: taking measures to respond to major network security risks facing power grids. Inform. Secur. Commun. Confident. 12, 73–78 (2019)

    Google Scholar 

  29. He, Z., Wang, Y., Liu, J.: The status quo of China’s network security grade protection and the study of 2.0 standard system. Inform. Technol. Netw. Secur. 38(3), 9–14 (2019)

    Google Scholar 

  30. Li, X., Liu, X., Gu, Q., Chen, B., Ge, L., Chen, G.: PCA and AHP hybrid risk assessment method for electronic transformer electric energy measurement and trade settlement”. Electr. Measur. Instrument. 55(14), 1–7 (2018)

    Google Scholar 

  31. Wang, B., Yin, J., Hu, S., Luo, Y., Pan, Y., Xiao, X.: Research on reliability allocation technology of smart energy meter based on AHP and group decision. Electr. Measur. Instrument. 58(12), 1–7 (2021)

    Google Scholar 

  32. Fu, G.: Analysis and research on probabilistic prediction of wind power based on bayesian method. North China Electric Power University (Beijing) (2019)

    Google Scholar 

  33. Wang, J.: Reliability analysis of power lines and their secondary equipment under multiple uncertain factors. University of Electronic Science and Technology of China (2020)

    Google Scholar 

  34. Zhang, J.: Research on decision optimization of renewable energy power project investment portfolio. Ph.D. Dissertation, North China Electric Power University (Beijing) (2020)

    Google Scholar 

  35. Liu, et al.: Evaluation model of network security situation based on support vector machine and self-adaptive weight. Computer Systems Applications (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Shenzhen Power Supply Co. Ltd., Shenzhen, 518001, China

    Weihua Li, Shaocheng Wu, Yue Ma & Tao Liu

  2. Department of Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Yali Bai

  3. School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Jinxin Zuo

Authors
  1. Weihua Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shaocheng Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yue Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tao Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yali Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jinxin Zuo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jinxin Zuo .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Xiaorui Zhang

  3. Jinan University, Guangzhou, China

    Zhihua Xia

  4. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, W., Wu, S., Ma, Y., Liu, T., Bai, Y., Zuo, J. (2022). Risk Assessment of Electric Energy Metering Automation System Based on Bayesian Network. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13340. Springer, Cham. https://doi.org/10.1007/978-3-031-06791-4_38

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-06791-4_38

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06790-7

  • Online ISBN: 978-3-031-06791-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation