Advertisement

Network Recovery for Large-Scale Failures in Smart Grid by Simulation

  • Huibin JiaEmail author
  • Hongda Zheng
  • Yonghe Gai
  • Dongfang Xu
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 295)

Abstract

Large-scale natural disaster or malicious attacks could cause serious damage to the power communication network in smart grid. If the damaged network cannot be repaired timely, great threat will be brought to the secure and stable operation of power grid. Therefore, an importance-based recovery method for large-scale failure has been proposed in smart grid by simulation. Firstly, the link importance for the whole network is calculated according to the solution of the link importance for the services type and the importance of services type for the power communication network. Secondly, a fault recovery model with the sum of the importance of each fault link has been established to recover more important communication services under the condition of limited resources. Finally, we propose a heuristic algorithm to reduce the expenditure of time, and then compare the results of the model with the 0–1 integer programming method to verify the feasibility of the method. The experimental results show that the links which carry high-priority can get priority to be repaired in the paper, thus it ensures the safe and stable operation of power communication network.

Keywords

Network recovery Smart grid Large-scale failures Simulation 

Notes

Acknowledgement

This work was supported by the Natural Science Foundation of China grant No. 61472037; the Fundamental Research Funds for the Central Universities 2017 MS113.

References

  1. 1.
    Cleveland, F.: Enhancing the reliability and security of the information infrastructure used to manage the power system. In: Power Engineering Society General Meeting. IEEE (2007)Google Scholar
  2. 2.
    Kamrul, I.M., Oki, E.: Optimization of OSPF link weight to minimize worst-case network congestion against single-link failure. In: IEEE International Conference on Communications. IEEE (2011)Google Scholar
  3. 3.
    Jiang, D., Wang, W., Shi, L., Song, H.: A compressive sensing-based approach to end-to-end network traffic reconstruction. IEEE Trans. Netw. Sci. Eng. (2018).  https://doi.org/10.1109/tnse.2018.2877597
  4. 4.
    Yu, H., Yang, C.: Partial network recovery to maximize traffic demand. IEEE Commun. Lett. 15, 1388–1390 (2011)CrossRefGoogle Scholar
  5. 5.
    Bartolini, N., et al.: Network recovery after massive failures. In: IEEE/IFIP International Conference on Dependable Systems and Networks, pp. 97–108. IEEE (2016)Google Scholar
  6. 6.
    Bartolini, N., et al.: On critical service recovery after massive network failures. IEEE/ACM Trans. Netw. 25, 2235–2249 (2017)CrossRefGoogle Scholar
  7. 7.
    Genda, K., Kamamura, S.: Multi-stage network recovery considering traffic demand after a large-scale failure. In: IEEE International Conference on Communications. IEEE (2016)Google Scholar
  8. 8.
    Izaddoost, A., Heydari, S.S.: Enhancing network service survivability in large-scale failure scenarios. J. Commun. Netw. 16, 534–547 (2014)CrossRefGoogle Scholar
  9. 9.
    Wang, J., Qiao, C., Yu, H.: On progressive network recovery after a major disruption. In: 2011 Proceedings IEEE INFOCOM, pp. 1925–1933 IEEE (2011)Google Scholar
  10. 10.
    Horie, T., et al.: A new method of proactive recovery mechanism for large-scale network failures In: International Conference on Advanced Information NETWORKING and Applications, pp. 951–958. IEEE (2009)Google Scholar
  11. 11.
    Jiang, D., Huo, L., Lv, Z., et al.: A joint multi-criteria utility-based network selection approach for vehicle-to-infrastructure networking. IEEE Trans. Intell. Transp. Syst. 19(10), 3305–3319 (2018)CrossRefGoogle Scholar
  12. 12.
    Berclaz, J., et al.: Multiple object tracking using k-shortest paths optimization. IEEE Trans. Pattern Anal. Mach. Intell. 33, 1806–1819 (2011)CrossRefGoogle Scholar
  13. 13.
    Fan, B., Tang, L.: Vulnerability analysis of power communication network. Proc. CSEE 34, 1191–1197 (2014)Google Scholar
  14. 14.
    Jiang, D., Wang, Y., Han, Y., et al.: Maximum connectivity-based channel allocation algorithm in cognitive wireless networks for medical applications. Neurocomputing 220, 41–51 (2017)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Huibin Jia
    • 1
    Email author
  • Hongda Zheng
    • 1
  • Yonghe Gai
    • 1
  • Dongfang Xu
    • 1
  1. 1.School of Electrical and Electronic EngineeringNorth China Electric Power UniversityBaodingChina

Personalised recommendations