2602003127
  • Open Access
  • Article

Power Supply Reliability Analysis of Distribution Network Based on Knowledge Graph

  • Wei Cheng 1,   
  • Yujia Zhang 2,   
  • Hamid Reza Karimi 3,   
  • Wanqing Song 1,*,   
  • Hongqing Zheng 1

Received: 16 Nov 2025 | Revised: 27 Jan 2026 | Accepted: 31 Mar 2026 | Published: 02 Jul 2026

Abstract

The reliability of the power supply is intrinsically linked to the robustness of the distribution network. Constructing a distribution network model is highly challenging due to its complex structure, the large number of equipment and users, and the randomness of load variations and equipment failures. Knowledge graphs, a structured data representation describing entities and their relationships, can address challenges by modelling various devices, users and real-time operation data as entities in a graphical manner. As a result, the constructed large network graphs are not only easy to interpret, but also efficient in data management and reliability analysis. The results are promising and validate our hypothesis.

References 

  • 1.

    Chen, B.; Qin, H.; Ding, J. A Reliability Forecasting Method for Distribution Network Based on Data Mining. In Proceedings of the 2018 IEEE China International Conference on Electricity Distribution, Tianjin, China, 17–19 September 2018.

  • 2.

    Zhong, L.; Wu, J.; Li, Q.; et al. A Comprehensive Survey on Automatic Knowledge Graph Construction. ACM Comput. Surv. 2023, 56, 94.

  • 3.

    Wang, L.; Sun, C.; Zhang, C.; et al. Application of knowledge graph in software engineering field: A systematic literature review. Inf. Softw. Technol. 2023, 164, 107327.

  • 4.

    Tang, X.; Chi, G.; Cui, L.; et al. Exploring Research on the Construction and Application of Knowledge Graphs for Aircraft Fault diagnosis. Sensors 2023, 23, 5295.

  • 5.

    Xiao, Y.; Zheng, S.; Shi, J.; et al. Knowledge graph-based manufacturing process planning: A state-of-the-art review. J. Manuf. Syst. 2023, 70, 417–435.

  • 6.

    Murali, L.; Gopakumar, G.; Viswanathan, D.M.; et al. Towards electronic health record-based medical knowledge graph construction, completion, and applications: A literature study. J. Biomed. Inform. 2023, 143, 104403.

  • 7.

    Liu, R.; Fu, R.; Xu, K.; et al. A Review of Knowledge Graph-Based Reasoning Technology in the Operation of Power Systems. Appl. Sci. 2023, 13, 4357.

  • 8.

    Zhou, B.; Gao, D.; Yan, L.; et al. Research on key technologies for fault knowledge acquisition of power communication equipment. Procedia Comput. Sci. 2021, 183, 479–485.

  • 9.

    Fei, Y.; Hongying, Y.; Gaoshang, Z. Research on Intelligent Construction Technology of Information-driven Power Grid Security Situation Knowledge Graph. IFAC-PapersOnline 2022, 55, 102–107.

  • 10.

    Qin, D.; Zheng, G.; Liu, L.; et al. Construction of knowledge graph of multi-source heterogeneous distribution network systems. In Proceedings of the 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE), Harbin, China, 25–27 December 2020; pp. 162–166.

  • 11.

    Tang, Y.; Liu, T.; Liu, G.; et al. Enhancement of power equipment management using knowledge graph. In Proceedings of the 2019 IEEE Innovative Smart Grid Technologies—Asia (ISGT Asia), Chengdu, China, 21–24 May 2019.

  • 12.

    Chun, S.; Jin, X.; Seo, S.; et al. Knowledge graph modeling for semantic integration of energy services. In Proceedings of the 2018 IEEE International Conference on Big Data and Smart Computing (BigComp), Shanghai, China, 15–17 January 2018.

  • 13.

    Fenf, Y.; Zhai, F.; Li, B.; et al. Research on intelligent fault diagnosis of power acquisition based on knowledge graph. In Proceedings of the 2019 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE), Xiamen, China, 18–20 October 2019.

  • 14.

    Feng, J.Q.; Smith, J.S.; Wu, Q.H.; et al. Condition assessment of power system apparatuses using ontology systems. In Proceedings of the 2005 IEEE/PES Transmission & Distribution Conference & Exposition: Asia and Pacific, Dalian, China, 18–18 August 2005.

  • 15.

    Liao, Z.; Liu, S. Substation alarm information processing based on ontology theory. In Proceedings of the 2015 5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), Changsha, China, 26–29 November 2015.

  • 16.

    Yan, L.; Tang, W.; Wu, Q.H.; et al. Kernel-based consensus clustering for ontology-embedded document repository of power substations. CSEE J. Power Energy Syst. 2017, 3, 212–221.

  • 17.

    Wang, H.; Cao, J.; Lin, D. Deep analysis of power equipment defect based on semantic framework text mining technology. CSEE J. Power Energy Syst. 2019, 8, 1157–1164.

  • 18.

    Zhang, Y.; Xu, W.; Yu, Z.; et al. Construction of Topic Hierarchy with Subtree Representation for Knowledge Graphs. Axioms 2025, 14, 300.

  • 19.

    Zhang, Y.; Pietrasik, M.; Xu, W.; et al. Hierarchical Topic Modelling for Knowledge Graphs. In The Semantic Web: ESWC 2022; Lecture Notes in Computer Science; Groth, P., Rula, A., Schneider, J., et al., Eds.; Springer: Cham, Switzerland, 2022; Volume 13261, pp. 237–252.

  • 20.

    Zhang, Y.; Sadler, T.; Taesiri, M.R.; et al. Fine-tuning Language Models for Triple Extraction with Data Augmentation. In Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024), Bangkok, Thailand, 15 August 2024; pp. 116–124.

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How to Cite
Cheng, W.; Zhang, Y.; Karimi, H. R.; Song, W.; Zheng, H. Power Supply Reliability Analysis of Distribution Network Based on Knowledge Graph. Nonlinear Analysis and Computer Simulations 2026, 1 (3), 11. https://doi.org/10.53941/nacs.2026.100011.
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