Grid structure optimization using slow coherency theory and holomorphic embedding method
This paper addresses the issue of complex fault oscillation modes and weak voltage points in large power systems by proposing a network structure optimization method that balances system synchrony and node voltage stability. The method uses slow synchrony clustering theory to establish node classifi...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
|
| Series: | International Journal of Electrical Power & Energy Systems |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061524005908 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832595373289373696 |
|---|---|
| author | Fei Tang Mo Chen Yuhan Guo Jinzhou Sun Xiaoqing Wei Jiaquan Yang Xuehao He |
| author_facet | Fei Tang Mo Chen Yuhan Guo Jinzhou Sun Xiaoqing Wei Jiaquan Yang Xuehao He |
| author_sort | Fei Tang |
| collection | DOAJ |
| description | This paper addresses the issue of complex fault oscillation modes and weak voltage points in large power systems by proposing a network structure optimization method that balances system synchrony and node voltage stability. The method uses slow synchrony clustering theory to establish node classification criteria and a comprehensive synchrony indicator for quantitative description of network synchrony performance. Simultaneously, it employs the holomorphic embedding method to solve the voltage sigma indicator for quantitative assessment of voltage stability. A model that considers both system synchrony and node voltage stability is then developed and optimized using a discrete particle swarm algorithm in simulations with 13-node, 118-node, and 2383-wp systems, compared to other classical algorithms. Simulation results show that the proposed optimization method effectively improves the synchrony clustering performance and voltage stability of the test systems, offering faster optimization speed and better results compared to other classical algorithms. |
| format | Article |
| id | doaj-art-0acb52e5a44b46e7acc090e00784ae43 |
| institution | Kabale University |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-0acb52e5a44b46e7acc090e00784ae432025-01-19T06:23:50ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-03-01164110367Grid structure optimization using slow coherency theory and holomorphic embedding methodFei Tang0Mo Chen1Yuhan Guo2Jinzhou Sun3Xiaoqing Wei4Jiaquan Yang5Xuehao He6School of Electrical and Automation, Wuhan University, Wuhan, China; Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, Wuhan, ChinaSchool of Electrical and Automation, Wuhan University, Wuhan, China; Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, Wuhan, China; Corresponding author at: School of Electrical and Automation, Wuhan University, Wuhan, China.School of Electrical and Automation, Wuhan University, Wuhan, China; Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, Wuhan, ChinaSchool of Electrical and Automation, Wuhan University, Wuhan, China; Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, Wuhan, ChinaState Grid Wuhu Power Supply Company, Wuhu, ChinaElectric Power Research Institute, Yunnan Power Grid Co., Ltd., Yunnan Province, ChinaElectric Power Research Institute, Yunnan Power Grid Co., Ltd., Yunnan Province, ChinaThis paper addresses the issue of complex fault oscillation modes and weak voltage points in large power systems by proposing a network structure optimization method that balances system synchrony and node voltage stability. The method uses slow synchrony clustering theory to establish node classification criteria and a comprehensive synchrony indicator for quantitative description of network synchrony performance. Simultaneously, it employs the holomorphic embedding method to solve the voltage sigma indicator for quantitative assessment of voltage stability. A model that considers both system synchrony and node voltage stability is then developed and optimized using a discrete particle swarm algorithm in simulations with 13-node, 118-node, and 2383-wp systems, compared to other classical algorithms. Simulation results show that the proposed optimization method effectively improves the synchrony clustering performance and voltage stability of the test systems, offering faster optimization speed and better results compared to other classical algorithms.http://www.sciencedirect.com/science/article/pii/S0142061524005908Grid structure optimizationHolomorphic embeddingSlow coherencyNode classificationVoltage Stability Margin |
| spellingShingle | Fei Tang Mo Chen Yuhan Guo Jinzhou Sun Xiaoqing Wei Jiaquan Yang Xuehao He Grid structure optimization using slow coherency theory and holomorphic embedding method International Journal of Electrical Power & Energy Systems Grid structure optimization Holomorphic embedding Slow coherency Node classification Voltage Stability Margin |
| title | Grid structure optimization using slow coherency theory and holomorphic embedding method |
| title_full | Grid structure optimization using slow coherency theory and holomorphic embedding method |
| title_fullStr | Grid structure optimization using slow coherency theory and holomorphic embedding method |
| title_full_unstemmed | Grid structure optimization using slow coherency theory and holomorphic embedding method |
| title_short | Grid structure optimization using slow coherency theory and holomorphic embedding method |
| title_sort | grid structure optimization using slow coherency theory and holomorphic embedding method |
| topic | Grid structure optimization Holomorphic embedding Slow coherency Node classification Voltage Stability Margin |
| url | http://www.sciencedirect.com/science/article/pii/S0142061524005908 |
| work_keys_str_mv | AT feitang gridstructureoptimizationusingslowcoherencytheoryandholomorphicembeddingmethod AT mochen gridstructureoptimizationusingslowcoherencytheoryandholomorphicembeddingmethod AT yuhanguo gridstructureoptimizationusingslowcoherencytheoryandholomorphicembeddingmethod AT jinzhousun gridstructureoptimizationusingslowcoherencytheoryandholomorphicembeddingmethod AT xiaoqingwei gridstructureoptimizationusingslowcoherencytheoryandholomorphicembeddingmethod AT jiaquanyang gridstructureoptimizationusingslowcoherencytheoryandholomorphicembeddingmethod AT xuehaohe gridstructureoptimizationusingslowcoherencytheoryandholomorphicembeddingmethod |