整合多个松总线操作和元启发技术,以优化功率流
Swaechchha Dahal1,2, Gunne John Hegglid3, Jonas Kristiansen4
1Department of Electrical, IT and Cybernetics, University of South Eastern Norway, Porsgrunn, Norway. swaechchha.dahal@usn.no.
Scientific reports
|May 14, 2025
概括
这项研究通过使用多重松总线操作 (MSO) 框架来提高电网效率. 在与MSO相结合时,灰狼优化 (GWO) 算法显著减少了超过13%的系统损失.
科学领域:
- 电气工程 电气工程
- 电力系统分析 分析 分析
背景情况:
- 现代能源网越来越复杂,需要进行现实的电流研究.
- 最佳功率流 (OPF) 分析对于电力系统的稳定性和效率至关重要.
研究的目的:
- 实施和评估多重松巴士操作 (MSO) 框架,以提高OPF的现实性和效率.
- 在MSO框架内对电力系统优化的metaheuristic算法 (PSO,CSA,GWO) 进行比较评估.
主要方法:
- 实施多重拖车运行 (MSO) 框架.
- 对粒子优化 (PSO),子搜索算法 (CSA) 和灰狼优化 (GWO) 的比较评估.
- 在各种负载条件下对减少的北欧44和IEEE基准系统的评估.
主要成果:
- 仅MSO框架就减少了5%的系统损失.
- 将灰狼优化 (GWO) 算法与MSO集成实现了最显著的总系统损失减少,额外减少8.3%.
- 在最大限度地减少系统损失,优化线路负载,电压角度和发电分布方面,GWO表现出卓越的性能.
结论:
- 灰狼优化 (GWO) 算法,在多重松总线操作 (MSO) 框架内,对于优化功率流量和减少系统损失非常有效.
- 该MSO框架与元启发算法相结合,为提高电力网络效率提供了显著的潜力.
- 这种方法为未来电力系统中先进的功率流量优化策略提供了基础.
关键词:
子搜索算法 子搜索算法灰狼优化优化 灰狼优化线路负载优化优化 线路负载优化超启发式算法 (Metaheuristic Algorithms) 是一种算法,可以通过多重松总线操作最佳的功率流量是最佳的.粒子群集优化优化 粒子群集优化减轻功耗损失,最大限度地减少功耗损失.电力系统的效率效率是什么减少了北欧44型号的模型.更多相关视频
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