使用最近的元启发算法对最佳功率流的全面分析.
Ahmed A Zaki Diab1, Ashraf M Abdelhamid2, Hamdy M Sultan3
1Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia, 61111, Egypt. a.diab@mu.edu.eg.
Scientific reports
|June 11, 2024
概括
本研究介绍了六个元启发算法,包括梯度基础优化器 (GBO),以有效地解决最佳功率流 (OPF) 问题. 梯度优化器在解决复杂的电力系统优化挑战方面表现出卓越的性能.
科学领域:
- 电气工程 电气工程
- 计算智能是一种计算智能.
- 优化技术 优化技术
背景情况:
- 最佳功率流 (OPF) 问题对于高效的电力系统运行至关重要.
- 现有的元启发算法在解决复杂的OPF配方方面面临着挑战.
- 标准和保守的操作条件需要强大的优化方法.
研究的目的:
- 介绍和评估六个用于解决最佳功率流 (OPF) 问题的元启发式算法.
- 在各种操作条件下评估这些算法的有效性和稳定性.
- 提出一种新的比较方法来评估优化技术.
主要方法:
- 实现了六个元启发算法:快速子搜索 (FCS),Salp Swarm算法 (SSA),动态控制子搜索 (DCCS),梯度基础优化器 (GBO),北方戈斯霍克优化 (NGO) 和对立流向算法 (OFDA).
- 用不同的目标,约束和表述建模OPF问题.
- 进行IEEE30总线和IEEE118总线标准测试系统的案例研究.
- 开发一个绩效评估程序和一个新的比较方法.
主要成果:
- 梯度优化器 (GBO) 在有效解决各种OPF问题方面显示出显著的潜力.
- 拟议的算法在标准测试系统上被评估其有效性和稳定性.
- 新的比较方法促进了对优化技术的彻底评估.
结论:
- 梯度优化器 (GBO) 是一种高效的算法,用于解决最佳功率流 (OPF) 问题.
- 该研究验证了对基准电力系统提出的元启发算法的稳定性.
- 开发的比较框架为未来对电力系统优化研究提供了有价值的工具.
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