Dynamic Identification of Critical Transmission Lines in Power Systems With Wind Power Integration Based on Maximum Influence Theory
With the integration of large-scale renewable energy sources, the risk of power system operation has increased significantly. To effectively prevent large-scale blackout incidents, it is essential to identify the critical transmission lines within the system accurately. However, most of the existing...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10829957/ |
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| author | Gang Ruan Changzheng Shao Hao Wang Yingxu Jin Lingzi Zhu Dongxu Chang |
| author_facet | Gang Ruan Changzheng Shao Hao Wang Yingxu Jin Lingzi Zhu Dongxu Chang |
| author_sort | Gang Ruan |
| collection | DOAJ |
| description | With the integration of large-scale renewable energy sources, the risk of power system operation has increased significantly. To effectively prevent large-scale blackout incidents, it is essential to identify the critical transmission lines within the system accurately. However, most of the existing identification methods are based on complex network theory and use static graphs to model the propagation of cascading faults, which cannot consider the impact of wind power output fluctuations. To address this issue, this paper introduces and improves the maximum influence model of the community network to identify the critical lines. By developing the time-sequence cascading faults graph, the proposed method can comprehensively consider the impact of wind power output fluctuations on the propagation of cascading faults. Based on the influence maximization model in time-sequence cascading faults graph (IMTG), the line fault influence calculation algorithm (LFIC) and improved critical line identification algorithm (ILIT) are proposed to identify the critical transmission lines. In order to verify the effectiveness of the proposed method, case studies are carried out on the IEEE 39-bus system and the IEEE 118-bus system. The results show that the critical lines identified by the proposed method can effectively cope with the fluctuation of wind power output in the future, and can provide timely feedback for power system operators. |
| format | Article |
| id | doaj-art-5160c1e619ee4f7fa25e5c1b0fbfee4c |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-5160c1e619ee4f7fa25e5c1b0fbfee4c2025-01-21T00:01:06ZengIEEEIEEE Access2169-35362025-01-0113106891070110.1109/ACCESS.2025.352666010829957Dynamic Identification of Critical Transmission Lines in Power Systems With Wind Power Integration Based on Maximum Influence TheoryGang Ruan0https://orcid.org/0009-0002-3098-4891Changzheng Shao1https://orcid.org/0000-0003-2130-4487Hao Wang2Yingxu Jin3Lingzi Zhu4Dongxu Chang5School of Electrical Engineering, Chongqing University, Chongqing, ChinaSchool of Electrical Engineering, Chongqing University, Chongqing, ChinaSchool of Electrical Engineering, Chongqing University, Chongqing, ChinaSchool of Electrical Engineering, Chongqing University, Chongqing, ChinaPower Dispatching Control Center, Guizhou Power Grid Company Ltd., Guiyang, ChinaElectric Power Research Institute, China Southern Power Grid, Guangzhou, ChinaWith the integration of large-scale renewable energy sources, the risk of power system operation has increased significantly. To effectively prevent large-scale blackout incidents, it is essential to identify the critical transmission lines within the system accurately. However, most of the existing identification methods are based on complex network theory and use static graphs to model the propagation of cascading faults, which cannot consider the impact of wind power output fluctuations. To address this issue, this paper introduces and improves the maximum influence model of the community network to identify the critical lines. By developing the time-sequence cascading faults graph, the proposed method can comprehensively consider the impact of wind power output fluctuations on the propagation of cascading faults. Based on the influence maximization model in time-sequence cascading faults graph (IMTG), the line fault influence calculation algorithm (LFIC) and improved critical line identification algorithm (ILIT) are proposed to identify the critical transmission lines. In order to verify the effectiveness of the proposed method, case studies are carried out on the IEEE 39-bus system and the IEEE 118-bus system. The results show that the critical lines identified by the proposed method can effectively cope with the fluctuation of wind power output in the future, and can provide timely feedback for power system operators.https://ieeexplore.ieee.org/document/10829957/Wind power integrationcritical transmission line identificationmaximal influence theorytime-sequence cascading faults graph (TCFG) |
| spellingShingle | Gang Ruan Changzheng Shao Hao Wang Yingxu Jin Lingzi Zhu Dongxu Chang Dynamic Identification of Critical Transmission Lines in Power Systems With Wind Power Integration Based on Maximum Influence Theory IEEE Access Wind power integration critical transmission line identification maximal influence theory time-sequence cascading faults graph (TCFG) |
| title | Dynamic Identification of Critical Transmission Lines in Power Systems With Wind Power Integration Based on Maximum Influence Theory |
| title_full | Dynamic Identification of Critical Transmission Lines in Power Systems With Wind Power Integration Based on Maximum Influence Theory |
| title_fullStr | Dynamic Identification of Critical Transmission Lines in Power Systems With Wind Power Integration Based on Maximum Influence Theory |
| title_full_unstemmed | Dynamic Identification of Critical Transmission Lines in Power Systems With Wind Power Integration Based on Maximum Influence Theory |
| title_short | Dynamic Identification of Critical Transmission Lines in Power Systems With Wind Power Integration Based on Maximum Influence Theory |
| title_sort | dynamic identification of critical transmission lines in power systems with wind power integration based on maximum influence theory |
| topic | Wind power integration critical transmission line identification maximal influence theory time-sequence cascading faults graph (TCFG) |
| url | https://ieeexplore.ieee.org/document/10829957/ |
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