access icon openaccess Knowledge graph-based method for identifying topological structure of low-voltage distribution network

This is an open access article published by the IET under the Creative Commons Attribution -NonCommercial License (http://creativecommons.org/licenses/by-nc/3.0/)
Received 29/12/2019, Accepted 02/09/2020, Revised 22/07/2020, Published 09/09/2020

The correct topological relationship is crucial in the low-voltage distribution network, as the actual topological structure of the low-voltage distribution network changes frequently and tremendously due to the need of operation and maintenance, and it cannot be correctly reflected upon failure in timely updating of data, low circulation, poor quality etc. therefore, it is necessary to identify the topology. The knowledge graph technology can clearly reflect the existing relationship between data, deducing and mining hidden knowledge, suitable for topology identification of the low-voltage distribution network. In the study, the knowledge graph technology was employed for topology identification: firstly, analyse the construction method of the knowledge graph, integrate data in multiple low-voltage distribution network information systems based on the knowledge graph technology, deduce missing data, find out the relationship between data, and then build the knowledge graph of low-voltage distribution network topological structure, and finally, based on ‘Typical Design Specification of Low-Voltage Distribution Network Infrastructure Project’ and semantic segmentation, identify the user–transformer relationship in low-voltage distribution network information system. The test results of the examples were very satisfactory, showing the theoretical values and practical application values of the identification method proposed in this study.

Inspec keywords: graph theory; distribution networks; power transformers; information systems; data mining

Other keywords: knowledge graph technology; actual topological structure; low-voltage distribution network topological structure; knowledge graph-based method; hidden knowledge; topology identification; user–transformer relationship; correct topological relationship; semantic segmentation; low-voltage distribution network infrastructure project; low-voltage distribution network information system

Subjects: Combinatorial mathematics; Transformers and reactors; Distribution networks; Data handling techniques; Combinatorial mathematics

References

    1. 1)
      • 12. Zhou, D., Zhang, M., Zhu, H., et al: ‘Research and application of intelligent distribution network dispatching interactive under the background of electric power system reformation’, Electr. Power Eng. Technol., 2018, 37, (2), pp. 89–94.
    2. 2)
      • 18. Shi, B., Weninger, T.: ‘Discriminative predicate path mining for fact checking in knowledge graphs’, Knowl.-Based Syst., 2016, 104, pp. 123–133.
    3. 3)
      • 9. Yan, Q., Wang, J., Huang, K., et al: ‘Analysis of equivalent topology and power- flow calculation model for pulsed power load’, Electr. Power Eng. Technol., 2018, 33, (23), pp. 5523–5531.
    4. 4)
      • 20. Wang, C., Ma, X., Chen, J., et al: ‘Information extraction and knowledge graph construction from geoscience literature’, Comput. Geosci., 2018, 112, pp. 112–120.
    5. 5)
      • 13. Liu, Q., Li, Y., Duan, H., et al: ‘Knowledge graph construction techniques’, J. Comput. Res. Dev., 2016, 53, (3), pp. 582–600.
    6. 6)
      • 1. Liu, Y., Zhang, N., Kang, C.: ‘A review on data-driven analysis and optimization of power grid’, Autom. Electr. Power Syst., 2018, 42, (6), pp. 157–167.
    7. 7)
      • 11. Sharon, Y., Annaswamy, A., Legbedji, M.A., et al: ‘Topology identification in distribution network with limited measurements’. 2012 IEEE PES Innovative Smart Grid Technologies, ISGT 2012, Washington, DC, USA., 2012, pp. 1–6.
    8. 8)
      • 24. Typical design of infrastructure project for distribution network of 10 kV and below in Guangzhou Power Supply Bureau.2017-9-30.
    9. 9)
      • 22. Yang, S., Han, R.: ‘A visual analysis of the status quo and trend of knowledge mapping research’, Intell. Data Work, 2012, 33, (4), pp. 22–28.
    10. 10)
      • 10. Li, Y., Feng, B., Li, G., et al: ‘Optimal distributed generation planning in active distribution networks considering integration of energy storage’, Appl. Energy, 2018, 210, pp. 1073–1081.
    11. 11)
      • 17. Uyar, A., Aliyu, F.M.: ‘Evaluating search features of Google knowledge graph and Bing satori’, Online Inf. Rev., 2015, 39, (2), pp. 197–213.
    12. 12)
      • 25. Xiao, Y., Zhao, Y., Tu, Z., et al: ‘Topology checking method for low voltage distribution network based on improved Pearson correlation coefficient’, Power Syst. Prot. Control, 2019, 47, (11), pp. 37–43.
    13. 13)
      • 7. Pappu, S.J., Bhatt, N., Pasumarthy, R., et al: ‘Identifying topology of low voltage (LV) distribution networks based on smart meter data’, IEEE Trans. Smart Grid, 2018, 9, pp. 5113–5122.
    14. 14)
      • 5. Luan, W., Peng, J., Maras, M., et al: ‘Distribution network topology error correction using smart meter data analytics’. 2013 IEEE Power and Energy Society General Meeting (PES), Vancouver, BC, Canada, 2013.
    15. 15)
      • 8. Lourenco, E.M., Coelho, E.P.R., Pal, B.C.: ‘Topology error and bad data processing in generalized state estimation’, IEEE Trans. Power Syst., 2015, 30, pp. 3190–3200.
    16. 16)
      • 16. Singh, M.: ‘Protection coordination in distribution systems with and without distributed energy resources – a review’, Prot. Control Mod. Power Syst., 2017, 2, (1), p. 27.
    17. 17)
      • 23. GB/T 23705-2009, Coding Rules for Place Names/Addresses of Digital City Geographic Information Public Platform.
    18. 18)
      • 15. Zhu, G., Iglesias, C.A.: ‘Sematch: semantic similarity framework for knowledge graphs’, Knowl.-Based Syst., 2017, 130, pp. 30–32.
    19. 19)
      • 3. Li, Y., Xu, L., Li, Y.: ‘Topology connecting strategy of the modular multilevel matrix converter for optimal switching times’, Electr. Power Eng. Technol., 2018, 33, (2), pp. 342–352.
    20. 20)
      • 2. Lang, Y., Li, J., Luo, Y., et al: ‘Large power grid topology analysis based on graph partitioning’, Power Syst. Prot. Control, 2017, 45, (23), pp. 108–115.
    21. 21)
      • 6. Ying, J., Mei, J., Wang, Y., et al: ‘Research on verification method of feeder topology model for distribution main station’, Power Syst. Prot. Control, 2018, 46, (7), pp. 83–89.
    22. 22)
      • 21. Shi, Z., Zhang, L., Hu, X., et al: ‘Power system transient stability rules extraction based on multi-attribute decision tree’, Trans. China Electrotech. Soc., 2019, 34, pp. 1–12. Available at https://doi.org/10.19595/j.cnki.1000-6753.tces.180648.
    23. 23)
      • 19. Li, X., Xu, J., Guo, Z., et al: ‘Construction and application of knowledge graph of power dispatching automation system’, Electr. Power, 2019, 52, pp. 1–8.
    24. 24)
      • 14. Liu, Z., Wang, H.: ‘Retrieval method for defect records of power equipment based on knowledge graph technology’, Autom. Electr. Power Syst., 2018, 42, (14), pp. 158–164.
    25. 25)
      • 4. Liao, Y., Weng, Y., Wu, M., et al: ‘Distribution grid topology reconstruction: an information theoretic approach’. North American Power Symp., Charlotte, NC, USA., 2015.
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