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Regular Paper | Open Access

Network Embedding Algorithm for Vulnerability Assessment of Power Transmission Lines Using Integrated Structure and Attribute Information

Xianglong Lian, Tong Qian, Zepeng Li, Xingyu Chen, Wenhu Tang ( ), Q. H. Wu
School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China
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Abstract

In power systems, failures of vulnerable lines can trigger large-scale cascading failures, and vulnerability assessment is dedicated to locating these lines and reducing the risks of such failures. Based on a structure and attribute network embedding (SANE) algorithm, a novel quantitative vulnerability analysis method is proposed to identify vulnerable lines in this research. First, a two-layered random walk network with topological and electrical properties of transmission lines is established. Subsequently, based on the weighted degree of nodes in the two-layered network, the inter-layer and intra-layer walking transition probabilities are developed to obtain walk sequences. Then, a Word2Vec algorithm is applied to obtain low-dimension vectors representing transmission lines, according to obtained walk sequences for calculating the vulnerability index of transmissions lines. Finally, the proposed method is compared with three widely used methods in two test systems. Results show the network embedding based method is superior to those comparison methods and can provide guidance for identifying vulnerable lines.

Keywords

Network embeddingrandom walktransmission linesvulnerability assessment

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CSEE Journal of Power and Energy Systems
Volume 10 Issue 1,
January 2024
Pages 351-360
DOI: 10.17775/CSEEJPES.2021.09630
Cite this article:
Lian X, Qian T, Li Z, et al. Network Embedding Algorithm for Vulnerability Assessment of Power Transmission Lines Using Integrated Structure and Attribute Information. CSEE Journal of Power and Energy Systems, 2024, 10(1): 351-360. https://doi.org/10.17775/CSEEJPES.2021.09630

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Received: 31 December 2021
Revised: 11 March 2022
Accepted: 12 April 2022
Published: 03 March 2023
© 2021 CSEE.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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