Research on the construction method of fault association knowledge graph for power grid operation based on multivariate heterogeneous data fusion

Shaoxin Chen; Mingxing Shi; Jinwang Chen; Shu Lin; Jinze Li; Ruyin Chen

doi:10.1117/12.3035243

19 July 2024 Research on the construction method of fault association knowledge graph for power grid operation based on multivariate heterogeneous data fusion

Shaoxin Chen, Mingxing Shi, Jinwang Chen, Shu Lin, Jinze Li, Ruyin Chen

Author Affiliations +

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132131C (2024) https://doi.org/10.1117/12.3035243
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

The power backbone network can be categorized into a transmission network, a data network, and a support network. Every network is equipped with an extensive array of communication and transmission equipment, produced by diverse manufacturers and consisting of intricate types of equipment. Locating the cause of a fault and identifying the specific equipment that is causing the problem can be challenging in the absence of a relationship map. Therefore, research is currently underway to construct a knowledge graph for correlation of power grid operation faults, utilizing a multi- heterogeneous data fusion method. By correlating knowledge, which encompasses fault cases, fault types, fault causes, and solutions, a logical relationship network is established to tackle issues pertaining to challenging fault cause localization, undefined fault impact scope, inconvenient fault information retrieval, and the inability to extract fault occurrence patterns.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shaoxin Chen, Mingxing Shi, Jinwang Chen, Shu Lin, Jinze Li, and Ruyin Chen "Research on the construction method of fault association knowledge graph for power grid operation based on multivariate heterogeneous data fusion", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132131C (19 July 2024); https://doi.org/10.1117/12.3035243

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KEYWORDS

Power grids

Telecommunications

Education and training

Data modeling

Feature extraction

Optical communications

Classification systems

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