Home Journals Articles Updates About
CN
image
收藏切换
Reactive Power Optimization of Wind Power Distribution Network Based on AG-MOPSO
收藏切换
PDF
Fuqing SU1, Honghai KUANG1, Hao ZHONG2
Journal of Power Supply | 2024, 22(4) : 192 - 199
Less
收藏切换
Journal of Power Supply | 2024, 22(4): 192-199
• Power System •
Reactive Power Optimization of Wind Power Distribution Network Based on AG-MOPSO
Full
Fuqing SU1, Honghai KUANG1, Hao ZHONG2
Affiliations
  • 1 College of Electrical and Information Engineering Hunan University of Technology Zhuzhou 412007 China
  • 2 China Three Gorges University Hubei Key Laboratory of Cascaded Hydropower Stations Operation and Control Yichang 443002 China
Published: 2024-07-30 doi: 10.13234/j.issn.2095-2805.2024.4.192
Outline
收藏切换

Aimed at uncertainties in the output from grid-connected wind turbine, the scenario analysis method based on probability occurrence is adopted to transform the uncertainty model into a multi-scenario problem with different occurrence probabilities, and a reactive power optimization model with the goal of minimizing the active power loss and voltage deviation is established. In view of the poor diversity of Pareto frontiers obtained using the traditional methods, an adaptive grid multi-objective particle swarm optimization (AG-MOPSO) algorithm is proposed, which uses adaptive grids to obtain the density of particles in external archives, selects the global optimal particles and maintains the scale of the external storage library according to the density information as well as the betting mechanism, thus effectively ensuring the uniformity and diversity of the Pareto frontier distribution. This algorithm is used to perform reactive power optimization calculations on an IEEE 33-bus system with wind power, and it is also compared with the existing NSGA-II algorithm. Results show that the Pareto frontier obtained using this algorithm is better, which verifies the feasibility of the proposed model and algorithm.

Scenario analysis  /  multi-objective reactive power optimization  /  adaptive grid  /  particle swarm optimization algorithm (PSO)  /  Pareto frontier
Fuqing SU, Honghai KUANG, Hao ZHONG. Reactive Power Optimization of Wind Power Distribution Network Based on AG-MOPSO[J]. Journal of Power Supply, 2024 , 22 (4) : 192 -199 . DOI: 10.13234/j.issn.2095-2805.2024.4.192
Funding
Less
  • National Natural Science Foundation(51977072)
  • Hubei Key Laboratory Open Fund(2019KJX06)
Appendix
Less
Year 2024 volume 22 Issue 4
PDF
353
129
Cite this Article
BibTeX
Article Info
doi: 10.13234/j.issn.2095-2805.2024.4.192
  • Receive Date：2021-09-01
  • Online Date：2025-07-21
  • Published：2024-07-30
Article Data
Affiliations
History
  • Received：2021-09-01
  • Revised：2021-10-27
  • Accepted：2021-12-02
Funding
National Natural Science Foundation(51977072)
Hubei Key Laboratory Open Fund(2019KJX06)
Affiliations
    1 College of Electrical and Information Engineering Hunan University of Technology Zhuzhou 412007 China
    2 China Three Gorges University Hubei Key Laboratory of Cascaded Hydropower Stations Operation and Control Yichang 443002 China
References
Share
https://castjournals.cast.org.cn/joweb/dyxb/EN/10.13234/j.issn.2095-2805.2024.4.192
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House