Abstract
The reliability optimization of power communication networks has become an important research in power communication. However, the research has not considered the value of historical data of network operation on the improvement of network reliability. As a result, amount of accumulated data in the network operation process has not played a role in improving network reliability. In order to solve this problem, based on the historical operational fault data, we analyze the characteristics of network faults from three aspects: historical data characteristics of network, equipment remaining service life and external factors of municipal construction. And we propose reliability improvement algorithm of power communication network based on network fault characteristics. In the simulation experiment, it is verified that the proposed algorithm has achieved good results in the reliability and communication efficiency improvement.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Yang, L., Sun, S.: Evaluation algorithm of node importance of power communication network. Smart Grid 7, 656–659 (2017)
Li, L.: Service-based node identification of power communication network. In: Electric Power Information and Communication Technology, vol. 6, pp. 62–66 (2018)
Sun, F., et al.: Research on routing optimization of power communication network based on improved genetic algorithm. Autom. Instrum. 6, 25–28 (2018)
Peng, L., Yaoqun, W., Chen, W.: Reliability optimization design of computer network based on genetic algorithm. Telecom World 9, 3–4 (2016)
Zeng, W., Yuan, Y.: Coordinated optimization method for power communication networks balancing reliability and communication efficiency. Electr. Autom. 4, 102–104 (2018)
Chen, S., et al.: Reliability evaluation of distribution network considering the probability distribution characteristics of irregular wind speed. Guangxi Electric Power, vol. 2, pp. 17–21 (2019)
Guo, J., et al.: Reliability evaluation of microgrid cyber physical system considering control function failure. Modern Electric Power, vol. 2, pp. 73–80 (2019)
Luming, W., Qiu, Y., Chen, Q.: The critical technology of the electric power telecommunication based on SDN. Jiangsu Electr. Eng. 3, 134–144 (2018)
Wang, Y., Zhang, Y., Wang, C.: A method of vulnerability assessment for power communication network based on mechanical model. Telecommun. Sci. 1, 54–61 (2019)
Brite. http://www.cs.bu.edu/brite/
Padmanabhan, V.N., Qiu, L., Wang, H.J.: Server-based inference of Internet link lossiness. In: IEEE INFOCOM 2003 Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No. 03CH37428), San Francisco, CA, vol. 1, pp. 145–155 (2003)
Zhou, M., Rui, L., Qiu, X., Xia, Z., Li, B.: Evaluation of the node importance in power grid communication network and analysis of node risk. In: NOMS 2018–2018 IEEE/IFIP Network Operations and Management Symposium, Taipei, pp. 1–5 (2018)
Acknowledgment
This work is supported by the Science and Technology Project of Guangdong Power Grid Co., Ltd: Research on ubiquitous business communication technology and service mode in smart grid distribution and consumption network-Topic 4: Research on smart maintenance, management and control technology in smart grid distribution and consumption communication network(GDKJXM20172950).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, R., Wang, F., Zhang, X., Chen, J., Tong, J. (2020). Reliability Improvement Algorithm of Power Communication Network Based on Network Fault Characteristics. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Lecture Notes in Computer Science(), vol 12240. Springer, Cham. https://doi.org/10.1007/978-3-030-57881-7_70
Download citation
DOI: https://doi.org/10.1007/978-3-030-57881-7_70
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-57880-0
Online ISBN: 978-3-030-57881-7
eBook Packages: Computer ScienceComputer Science (R0)