• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
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  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 59 Issue 5
Oct.  2024
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Article Contents
FAN Wenli, YE Yurun, LI Quanyou, XIAO Yeqi, XIONG Liying, HE Xiaofeng. Identification of Vulnerable Lines in Power Grid Based on Power Flow Correlation Network[J]. Journal of Southwest Jiaotong University, 2024, 59(5): 1006-1013. doi: 10.3969/j.issn.0258-2724.20210535
Citation: FAN Wenli, YE Yurun, LI Quanyou, XIAO Yeqi, XIONG Liying, HE Xiaofeng. Identification of Vulnerable Lines in Power Grid Based on Power Flow Correlation Network[J]. Journal of Southwest Jiaotong University, 2024, 59(5): 1006-1013. doi: 10.3969/j.issn.0258-2724.20210535

Identification of Vulnerable Lines in Power Grid Based on Power Flow Correlation Network

doi: 10.3969/j.issn.0258-2724.20210535
  • Received Date: 30 Jun 2021
  • Rev Recd Date: 03 Mar 2022
  • Available Online: 17 Jun 2024
  • Publish Date: 27 Apr 2022
  • Global blackouts have indicated that vulnerable lines in the power system would bring great operation risk and threaten system security. In view of this, the identification of vulnerable lines in the power system was carried out according to the transfer characteristics of the active power flows in the case of a two-stage cascading failure. First, from the perspective of system operation, the relationship between the lines was weighted in terms of the transferred active power flows when the power system had a two-stage cascading failure, and a bidirectional weighted power flow correlation network was constructed. Then, an improved E-index-based identification method for vulnerable lines was proposed to quantify line vulnerability in cascading failures. The results show that the average value at risk and the average conditional value at risk of the top eight vulnerable lines in the identified results are 3 453.73 MW and 187.82 MW respectively for the IEEE-39 bus system, which are much higher than those obtained from other methods. Meanwhile, the residual load rate of the system is only 43.4% after the static intentional attack, and the number of power islands in the system increases rapidly.

     

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