对许多客观电力流量问题进行精子群群优化,并加强电力系统的性能评估
Wulfran Fendzi Mbasso1,2, Ambe Harrison3,4, Pradeep Jangir5,6,7,8
1Technology and Applied Sciences Laboratory, U.I.T. of Douala, University of Douala, P.O. Box 8689, Douala, Cameroon. fendzi.wulfran@yahoo.fr.
Scientific reports
|May 18, 2025
概括
一个新的精子群集优化 (SSO) 算法MaOSSO提高了电力系统的效率,用于解决多目标最佳功率流 (MaO-OPF) 问题. 它实现了更快的融合和更短的计算时间,改善了可持续的运营.
科学领域:
- 电气工程 电气工程
- 计算智能是一种计算智能.
- 优化算法 优化算法
背景情况:
- 电力系统中的多目标最佳功率流 (MaO-OPF) 问题面临着因其高维,相互矛盾的目标而导致的融合,多样性和计算效率方面的挑战.
- 现有的多目标优化算法很难有效地解决大规模电力系统中的这些复杂性.
研究的目的:
- 引入一个先进的优化框架,即以生物系统为灵感的多目标精子群优化 (MaOSSO) 算法.
- 为了提高MaO-OPF问题的解决方案质量,融合速度和计算效率.
主要方法:
- 开发了MaOSSO算法,结合了适应多样性机制和群体智能超动态控制.
- 在DTLZ和MaF测试套件上对最先进的算法 (NSGA-III,RVEA) 进行MaOSSO测试.
- 验证了现实的IEEE 30,57,118总线电源系统的框架,优化功率损耗,电压稳定性,排放和运营成本.
主要成果:
- 与竞争方法相比,MaOSSO表现出卓越的性能,实现了高达15-20%的更快的融合和25%更短的计算时间.
- 该算法通过生物启发的多方向搜索策略有效地平衡了勘探和开发.
- 使用超量 (HV) 和代际距离等指标进行的全面评估证实了MaOSSO的稳健性和灵活性.
结论:
- 马奥索为适应性,智能和可持续的电力系统运行提供了强大而灵活的方法.
- 该算法在解决复杂的MaO-OPF挑战方面明显优于现有的基于群体的方法 (GWO,MOPSO,MOGWO).
- 未来的工作将专注于对极大规模系统的进一步改进.
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