LAPSE:2023.15646
Published Article
LAPSE:2023.15646
Identification of Key Nodes in a Power Grid Based on Modified PageRank Algorithm
Darui Zhu, Haifeng Wang, Rui Wang, Jiandong Duan, Jing Bai
March 2, 2023
Abstract
For avoiding the occurrence of large-scale blackouts due to disconnected nodes in the power grid, a modified PageRank algorithm is proposed to identify key nodes by integrating the topological information and node type. The node betweenness index is first introduced based on complex network theory, which is modified to reflect the node topological information in the power grid. Then, according to the characteristics of different node types in the power grid, a modified PageRank algorithm is proposed to rapidly identify key nodes, which takes the generator nodes, load nodes, and contact nodes into account. IEEE 39-Bus system and IEEE 118-Bus system are used for the simulations. Simulation results showed that the network transmission efficiencies of the power grid are reduced from 64.23% to 5.62% and from 45.4% to 5.12% in the two simulation systems compared with other methods. The proposed identification algorithm improved the accuracy, and a provincial power grid simulation system in China is used to verify the feasibility and validity. The identified nodes are removed, which split the power grid according to importance index values. The proposed method in this paper is helpful to prevent the occurrence of cascading failure in the power system, and it can also be used to power systems with renewable energy sources and an AC/DC hybrid power grid.
Keywords
complex network, empirical verification, modified PageRank algorithm, node betweenness, transmission efficiency
Suggested Citation
Zhu D, Wang H, Wang R, Duan J, Bai J. Identification of Key Nodes in a Power Grid Based on Modified PageRank Algorithm. (2023). LAPSE:2023.15646
Author Affiliations
Zhu D: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China [ORCID]
Wang H: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Wang R: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Duan J: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China [ORCID]
Bai J: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Journal Name
Energies
Volume
15
Issue
3
First Page
797
Year
2022
Publication Date
2022-01-22
ISSN
1996-1073
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Original Submission
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PII: en15030797, Publication Type: Journal Article
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https://doi.org/10.3390/en15030797
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