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

doi:10.17775/CSEEJPES.2021.09630

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

Regular Papers | 更新时间：2026-10-15

- Network Embedding Algorithm for Vulnerability Assessment of Power Transmission Lines Using Integrated Structure and Attribute Information
- CSEE Journal of Power and Energy Systems Vol. 10, Issue 1, Pages: 351-360(2024)
- 作者机构：
  
  School of Electric Power Engineering, South China University of Technology,Guangzhou,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2021.09630
  CLC：
- Published：01 January 2024
- 稿件说明：
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Xianglong Lian, Tong Qian, Zepeng Li, et al. Network Embedding Algorithm for Vulnerability Assessment of Power Transmission Lines Using Integrated Structure and Attribute Information[J]. CSEE Journal of Power and Energy Systems, 2024, 10(1): 351-360.
DOI：

Xianglong Lian, Tong Qian, Zepeng Li, et al. Network Embedding Algorithm for Vulnerability Assessment of Power Transmission Lines Using Integrated Structure and Attribute Information[J]. CSEE Journal of Power and Energy Systems, 2024, 10(1): 351-360. DOI： 10.17775/CSEEJPES.2021.09630.

摘要

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.

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