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Research on distribution network fault processing technology based on knowledge of graph.
- Source :
-
PloS one [PLoS One] 2023 Dec 14; Vol. 18 (12), pp. e0295421. Date of Electronic Publication: 2023 Dec 14 (Print Publication: 2023). - Publication Year :
- 2023
-
Abstract
- Safety and reliability are the basis of the development of a distribution network. To analyze the risk transmission process in the distribution network and ensure the safe and reliable operation of the power system, this paper intends to use the knowledge graph method to realize the risk analysis of the distribution network information system. Firstly, the knowledge graph method is used to extract and integrate the risk knowledge of the multi-dimensional information collected by the distribution network. Secondly, the knowledge graph model of distribution network risk analysis is constructed, and the multi-dimensional distribution network fault handling and knowledge graph construction oriented to the feeder and platform area are designed. The distribution line parameters of the low-voltage distribution network model, neutral point grounding mode, and different fault types are analyzed, and the non-planned island is searched based on the knowledge graph adjacency matrix. Finally, combined with the simulation experiment, it is verified that the proposed method can effectively depict the information risk process of the distribution network. The structure of this paper starts from the multi-node complex distribution network, combined with a knowledge graph and deep learning method, which can solve the distribution network fault more quickly.<br />Competing Interests: The authors have declared that no competing interests exist.<br /> (Copyright: © 2023 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
- Subjects :
- Reproducibility of Results
Computer Simulation
Technology
Cognition
Knowledge
Subjects
Details
- Language :
- English
- ISSN :
- 1932-6203
- Volume :
- 18
- Issue :
- 12
- Database :
- MEDLINE
- Journal :
- PloS one
- Publication Type :
- Academic Journal
- Accession number :
- 38096310
- Full Text :
- https://doi.org/10.1371/journal.pone.0295421