Research on an Algorithm of Power System Node Importance Assessment Based on Topology–Parameter Co-Analysis

Guowei Sun, Xianming Sun (), Junqi Geng and Guangyang Han
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Guowei Sun: School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China
Xianming Sun: School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China
Junqi Geng: State Grid Shandong Electric Power Company Zibo Power Supply Company, Zibo 255000, China
Guangyang Han: School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China

Energies, 2025, vol. 18, issue 14, 1-13

Abstract: As power grids continue to expand in scale, the occurrence of cascading failures within them can lead to significant economic losses. Therefore, assessing the criticality of grid nodes is crucial for ensuring the secure and stable operation of power systems and for mitigating losses when cascading failures occur. The classical Local Link Similarity (LLS) algorithm in complex networks evaluates the importance of network nodes from a neighborhood topology perspective, but it suffers from issues such as the excessive weighting of node degrees and the neglect of electrical parameters. Based on the classical algorithm, this paper first develops the Improved Local Link Similarity (ILLS) algorithm by substituting alternative similarity metrics and comparatively evaluating their performance. Building upon the ILLS, we then propose the Electrical LLS (ELLS) algorithm by integrating node power flow and electrical coupling connectivity as multiplicative factors, with optimal combinations determined via simulation experiments. Compared to classical approaches, ELLS demonstrates superior adaptability to power grid contexts and delivers enhanced accuracy in power system node importance assessments. These algorithms are applied to rank the node importance in the IEEE 300-bus system. Their performance is evaluated using the loss-of-load-size metric, comparing ELLS, ILLS, and the classical algorithm. The results demonstrate that under the loss-of-load-size metric, the ELLS algorithm achieves approximately 25% higher accuracy compared to both the ILLS and the classical algorithm, validating its effectiveness.

Keywords: node importance; node similarity; electrical coupling connectivity; complex networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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