对分布系统中可再生能源控制的优化方法的多标准评估
Ahmad Eid1, Abdulrahman Alsafrani2
1Department of Electrical Engineering, College of Engineering, Qassim University, Buraidah, 52571, Saudi Arabia.
Scientific reports
|October 17, 2025
概括
本研究使用10个性能指标对电力系统进行了统计评估,对20种超启发式优化技术进行了统计评估. 像AEO和GWO这样的算法在提高系统效率和减少功耗损失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:
- 电力系统需要优化以提高性能和整合可再生能源 (RES).
- 存在许多元启发式优化技术,但对于电力系统应用缺乏全面的统计比较.
研究的目的:
- 统计评估和排名20个电力系统的元启发式优化技术.
- 根据功耗损失,电压配置,负载流频率和执行时间来评估算法性能.
主要方法:
- 使用10个性能指标对20个元启发算法的统计评估.
- 在10个分布系统中应用弗里德曼排名和复合排名方法.
- 对33公交和69公交系统的功率损耗与已发表的结果进行比较.
主要成果:
- 像AEO,GWO和PSO这样的算法在"优秀"类别中获得了最高排名 (低于25%).
- ALO,DA和SSA被归类为"非常好" (排名为25-50%).
- CStA,HHO和AOS被列为"公平"类别 (75%以上).
结论:
- 该研究提供了用于电力系统优化的元启发式算法的强大排名.
- 选择合适的算法大大节省了操作员的时间,提高了系统性能,并提高了可用性.
- 性能最好的算法可以大幅减少功耗损失,正如案例研究所证明的那样.
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