Bayesian-Based Efficient Fault Location Algorithm for Power Bottom-Guaranteed Communication Networks
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In order to solve the problem of low fault location performance of power bottom-guaranteed communication network under uncertain environment, combined with the characteristics of fault location of power bottom-guaranteed communication network under uncertain environment, the two stages of fault detection and fault location are combined. This paper proposes a Bayesian-based efficient fault location algorithm for power bottom-guaranteed communication networks (BFLA). In the fault detection phase, the probe dependency matrix model is constructed, and a heuristic probe selection algorithm based on vector expansion theory is proposed. The detection result set is obtained by transmitting the probe. In the fault location stage, a Bayesian fault location model is constructed based on the set of detection results to solve the optimal suspected fault set. In the performance analysis part, compared with the existing algorithms, it is verified that the proposed algorithm effectively improves the accuracy of fault diagnosis and reduces the false positive rate.
KeywordsPower bottom-guaranteed communication network Fault location Bayesian
This work is supported by the Project on Research and Demonstration of Application Technology of Intelligent Management and Dynamic Simulation Based on Bottom-guaranteed Power Grid Communication System. Under Grant No. GDKJXM20180249 (036000KK52180006).
- 1.Yinzhao, Z., Xing, C., Jiming, L.: Telecommunications for electric power system. 27(168), 58–61 (2006)Google Scholar
- 4.Steinder, M., Sethi, A.S.: Increasing robustness of fault localization through analysis of lost, spurious, and positive symptoms. In: Proceedings of Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 1, pp. 322–331. IEEE (2002)Google Scholar
- 7.Dong, H.J.: Research and Implementation of Fault Localization Algorithm for IP-Based Networks Using Bayesian Networks. Beijing University of Posts and Telecommunications, Beijing (2009)Google Scholar
- 8.Narasimha, R., Dihidar, S., Ji, C., et al.: Scalable fault diagnosis in IP networks using graphical models: a variational inference approach. In: 2007 IEEE International Conference on Communications, pp. 147–152. IEEE (2007)Google Scholar
- 10.Meng, L.M., Huang, T., Cheng, L., et al.: Probe station placement for multiple faults localization. J. Beijing Univ. Posts Telecommun. 32(5), 1–5 (2009)Google Scholar
- 11.Winick, J., Jamin, S.: Inet-3.0: Internet Topology Generator. University of Michigan, Ann Arbor, MI (2002)Google Scholar