Skip to main content

Impact Analysis of False Data Injection Attack on Smart Grid State Estimation Under Random Packet Losses

  • Conference paper
  • First Online:
Recent Featured Applications of Artificial Intelligence Methods. LSMS 2020 and ICSEE 2020 Workshops (LSMS 2020, ICSEE 2020)

Abstract

Supervisory control and data acquisition (SCADA) system has been widely used in traditional power systems for operation and control. As increasingly more ICT technologies are deployed to improve the smartness of the power grid, cyber security is becoming an important issue in the development of smart grids, for example, false data injection attack (FDIA) poses a serious threat. The paper analyzes the impact of false data injection attack on smart grid state estimation under random packet losses. First, a measurement model of power grids under random packet loss is established, and an attack vector range that can fool the attack detector is acquired. Then, a mean square error matrix of weighted least squares estimation is proposed, taking into account potential false data injection attacks. A IEEE-14 nodes system is used to evaluate the performance of the weighted least squares state estimation under three different scenarios, namely false data injection attack only, random packet loss only, and under both random packet loss and false data injection attack.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Yan, J., Guo, F., Wen, C.: False data injection against state estimation in power systems with multiple cooperative attackers. ISA Trans. 101(10), 225–233 (2020)

    Article  Google Scholar 

  2. Sahoo, S., Dragicevic, T., Blaabjerg, F.: Cyber security in control of grid-tied power electronic converterschallenges and vulnerabilities. IEEE J. Emer. Sel. Top. Power Electr. 15, 1–15 (2019)

    Google Scholar 

  3. Shu, J., Guo, Z., Han, B.: A bilevel optimization model for power network spurious data injection attack. Autom. Electr. Power Syst. 43(10), 95–101 (2019)

    Google Scholar 

  4. Liang, G., Zhao, J., Luo, F., Weller, S.R., Dong, Z.Y.: A review of false data injection attacks against modern power systems. IEEE Trans. Smart Grid 8(4), 1630–1638 (2017)

    Article  Google Scholar 

  5. Liang, G., Weller, S.R., Zhao, J., Luo, F., Dong, Z.Y.: The 2015 Ukraine blackout: implications for false data injection attacks. IEEE Trans. Power Syst. 32(4), 3317–3318 (2017)

    Article  Google Scholar 

  6. Gong, X.: Analysis of the situation of the power outage in Venezuela and recommendations for the safety of critical infrastructure. J. Inf. Technol. Network Secur. 38(04), 1–2+14 (2019)

    Google Scholar 

  7. Gong, X.: Upadhyay, D., Sampalli, S.: Scada (supervisory control and data acquisition) systems: Vulnerability assessment and security recommendations. Comput. Secur. 89, 101666 (2020)

    Google Scholar 

  8. Liu, Y., Ning, P., Reiter, M.K.: False data injection attacks against state estimation in electric power grids. ACM Trans. Inf. Syst. Secur. 14(1), 1–33 (2011)

    Article  Google Scholar 

  9. Li, L., Yang, H., Xia, Y., Yang, H.: Event-based distributed state estimation for linear systems under unknown input and false data injection attack. Signal Process. 170, 107423 (2020)

    Article  Google Scholar 

  10. Guo, Z., Shi, D., Johansson, K., Shi, L.: Optimal linear cyber-attack on remote state estimation. IEEE Trans. Control Network Syst. 4, 4–13 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  11. Zhao, Y., Goldsmith, A., Vincent Poor, H.: Minimum sparsity of unobservable power network attacks. IEEE Trans. Autom. Control 62(7), 3354–3368 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  12. Kim, J., Lang, T., Thomas, R.J.: Subspace methods for data attack on state estimation: a data driven approach. IEEE Trans. Signal Process. 63(5), 1102–1114 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  13. Zhong, H., Du, D., Li, C., Li, X.: A novel sparse false data injection attack method in smart grids with incomplete power network information. Complexity 1–16 (2018)

    Google Scholar 

  14. Muniraj, D., Farhood, M.: Detection and mitigation of actuator attacks on small unmanned aircraft systems. Control Eng. Pract. 83, 188–202 (2019)

    Article  Google Scholar 

  15. Huaye, P., Chen, P., Hongtao, S., Mingjin, Y.: Incremental detection mechanism of microgrid under false data injection attack. Inf. Control 48(5), 522–527 (2019)

    Google Scholar 

  16. Chen, R., Li, X., Zhong, H., Fei, M.: A novel online detection method of data injection attack against dynamic state estimation in smart grid. Neurocomputing 344, 73–81 (2019)

    Article  Google Scholar 

  17. Du, D., Chen, R., Li, X., Wu, L., Zhou, P., Fei, M.: Malicious data deception attacks against power systems: a new case and its detection method. Trans. Inst. Measur. Control 41(6), 1590–1599 (2019)

    Article  Google Scholar 

  18. Du, D., Li, X., Li, W., Chen, R., Fei, M., Wu, L.: ADMM-based distributed state estimation of smart grid under data deception and denial of service attacks. IEEE Trans. Syst. Man Cybernet.-Syst. 49(8), 1698–1711 (2019)

    Article  Google Scholar 

  19. Xia, M., Du, D., Fei, M., Li, X., Yang, T.: A novel sparse attack vector construction method for false data injection in smart grids. Energies 13(11) (2020)

    Google Scholar 

  20. Aghanoori, N., Masoum, M.A., Abu-Siada, A., Islam, S.: Enhancement of microgrid operation by considering the cascaded impact of communication delay on system stability and power management. Int. J. Electr. Power Energy Syst. 120, 105964 (2020)

    Article  Google Scholar 

  21. Ding, D., Han, Q.L., Xiang, Y., Ge, X., Zhang, X.M.: A survey on security control and attack detection for industrial cyber-physical systems. Neurocomputing 275, 1674–1683 (2018)

    Article  Google Scholar 

Download references

Acknowledgement

Supported by Natural Science Foundation of China (No. 61633016, 61533010), Key Project of Science and Technology Commission of Shanghai Municipality (No. 19510750300, 19500712300, 16010500300), Industrial Internet Innovation and Development Project (TC190H3WL).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Minrui Fei .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Xia, M., Du, D., Fei, M., Li, K. (2020). Impact Analysis of False Data Injection Attack on Smart Grid State Estimation Under Random Packet Losses. In: Fei, M., Li, K., Yang, Z., Niu, Q., Li, X. (eds) Recent Featured Applications of Artificial Intelligence Methods. LSMS 2020 and ICSEE 2020 Workshops. LSMS ICSEE 2020 2020. Communications in Computer and Information Science, vol 1303. Springer, Singapore. https://doi.org/10.1007/978-981-33-6378-6_5

Download citation

  • DOI: https://doi.org/10.1007/978-981-33-6378-6_5

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-33-6377-9

  • Online ISBN: 978-981-33-6378-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics