Education, Science, Technology, Innovation and Life
Open Access
Sign In
  1. Home
  2. Journals
  3. Computing, Performance and Communication Systems
  4. Vol 9, Issue 1, 2025

Research on Fault Diagnosis and Disposal Suggestions Method of Power Communication Network Based on Dynamic Event Driven Knowledge Graph

Download as PDF

DOI: 10.23977/cpcs.2025.090111 | Downloads: 0 | Views: 22

Author(s)

Miaomiao Li 1, Lei Sheng 1, Hao Feng 1, Lingxiao Zhao 1, Yan Liu 1

Affiliation(s)

1 Information & Telecommunication Company, State Grid Henan Electric Power Company, Zhengzhou, China

Corresponding Author

Miaomiao Li

ABSTRACT

With the development of smart grids, the intelligent operation and maintenance of power communication networks urgently need to evolve from passive response to active warning. Although knowledge graphs provide a global perspective for this, their static characteristics are difficult to cope with real-time and changing network states. The article paper aims to address the limitations of static knowledge graphs in real-time fault diagnosis. The proposed method converts real-time monitoring data into timestamp events and constructs spatiotemporal correlation sessions, achieving active perception and collaborative analysis of multi-source asynchronous faults. Based on this method, the system can not only accurately locate the root cause of faults, but also generate interpretable disposal suggestions automatically based on topology and business logic, providing new ideas for building intelligent power communication operation and maintenance systems.

KEYWORDS

Power Communication, Graph Calculation, Fault Diagnosis, Random Walk

CITE THIS PAPER

Miaomiao Li, Lei Sheng, Hao Feng, Lingxiao Zhao, Yan Liu, Research on Fault Diagnosis and Disposal Suggestions Method of Power Communication Network Based on Dynamic Event Driven Knowledge Graph. Computing, Performance and Communication Systems (2025) Vol. 9: 78-87. DOI: http://dx.doi.org/10.23977/cpcs.2025.090111.

REFERENCES

[1] R. Ma, H. H. Chen, Y. R. Huang, and W. Meng, "Smart Grid Communication: Its Challenges and Opportunities," IEEE Transactions on Smart Grid, vol. 4, no. 1, pp. 36-46, 2013.
[2] T. Strasser et al., "A Review of Architectures and Concepts for Intelligence in Future Electric Energy Systems," IEEE Transactions on Industrial Electronics, vol. 62, no. 4, pp. 2424-2438, 2015.
[3] N. Zhang, H. Liu, L. Sheng, L. Wu, and Z. Wang, "Research on Risk Warning Technology of Electric Power Backbone Communication Network Based on Knowledge Graph," in 2023 IEEE 3rd International Conference on Power, Electronics and Computer Applications (ICPECA), 2023, pp. 764-768.
[4] S. Ji, S. Pan, E. Cambria, P. Marttinen, and P. S. Yu, "A Survey on Knowledge Graphs: Representation, Acquisition, and Applications," IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 2, pp. 494-514, 2022.
[5] G. Dequan, Z. Pengyu, W. Sheng, and Z. Ziyan, "Deep Learning-Based Fault Knowledge Graph Construction for Power Communication Networks," in 2024 6th Asia Energy and Electrical Engineering Symposium (AEEES), 2024, pp. 1088-1093.
[6] M. Cui, X. Y. Wu, and H. W. Zhu, "Intelligent diagnosis method for power communication equipment faults based on knowledge graph," Technology and Industry, vol. 24, no. 05, pp. 212-221, 2024.
[7] L. Yang et al., "Collective Entity Alignment for Knowledge Fusion of Power Grid Dispatching Knowledge Graphs," IEEE/CAA Journal of Automatica Sinica, vol. 9, no. 11, pp. 1990-2004, 2022.
[8] J. Chen, Y. Jiang, W. Lu, M. Han, and X. Li, "A large scale power communication network simulation system based on big graph database," Science China Technological Sciences, vol. 62, no. 12, pp. 2088-2092, 2019.
[9] L. Yi, Y. Yinbin, Z. Yang, H. Qinran, and D. Xing, "Construction Method of Equipment Knowledge Graph in Power Grid Monitoring Field Based on Multi-source Data Fusion," in 2021 IEEE Sustainable Power and Energy Conference (iSPEC), 2021, pp. 3637-3641.
[10] Z. G. Chen, B. Zhou, Z. Z. Qiao, and C. Hu, "Construction method of intelligent analysis rule library for distribution network monitoring information based on machine learning," Computing Technology and Automation, vol. 41, no. 03, pp. 148-153, 2022.
[11] H. Wu et al., "Construction of Power Communication Network Knowledge Graph with BERT-BiLSTM-CRF Model Based Entity Recognition," in 2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS), 2021, pp. 632-636.
[12] Y. J. Yang, B. Xu, and J. W. Hu, "An Accurate and Efficient Method for Constructing Domain Knowledge Graph," Journal of Software, vol. 29, no. 10, pp. 2931-2947, 2018.
[13] H. Zhao et al., "Effective Fault Scenario Identification for Communication Networks via Knowledge-Enhanced Graph Neural Networks," IEEE Transactions on Mobile Computing, vol. 23, no. 4, pp. 3243-3258, 2024.
[14] B. Perozzi, R. AI-Rfou, and S. Skiena, "DeepWalk: Online Learning of Social Representation," in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, New York, USA, 2014.
[15] A. Grover, "node2vec: Scalable Feature Learning for Networks," presented at the the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016. 
[16] M. Sedghi, A. Beckus, and G. K. Atia, "Sketch-based Community Detection via Representative Node Sampling," in 2020 25th International Conference on Pattern Recognition (ICPR), 2021, pp. 9851-9858.

Subscription
E-Mail Alert
Most popular papers
Call for Special Issue
Publication Ethics & OA Statement
Plagiarism Policy
Contact Us
Downloads: 3343
Visits: 207417

Sponsors, Associates, and Links

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.