综合传输扩展规划包括故障电流限制装置和使用元启发式优化技术进行螺旋控制的连续补偿
Abdulaziz Almalaq1, Khalid Alqunun1, Rabeh Abbassi1
1Department of Electrical Engineering, College of Engineering, University of Hail, 55473, Hail, Saudi Arabia.
Scientific reports
|June 6, 2024
概括
这项研究评估了10个传输扩展规划 (TEP) 的元启发算法. 开普勒优化算法 (KOA) 提供了卓越的解决方案质量,对于可靠的动力系统至关重要.
科学领域:
- 电气工程 电气工程
- 优化理论 优化理论
- 计算智能是一种计算智能.
背景情况:
- 传输扩展规划 (TEP) 对可靠和高效的电力系统至关重要.
- 优化TEP涉及复杂的数学编程和元启发式技术.
- 评估各种优化算法对于推进 TEP 方法是必不可少的.
研究的目的:
- 评估最近十个 TEP 问题的元启发式算法的性能.
- 在解决方案质量,融合速度和可扩展性方面比较算法效率.
- 在不同复杂的网络中确定TEP最有效的算法.
主要方法:
- 应用了十个元启发算法:Sinh Cosh优化器,Walrus优化器,雪算法,三角形拓聚合优化器,电养优化,开普勒优化算法 (KOA),泥虫优化器,海马优化器,特殊相对论搜索和白优化器 (WSO).
- 采用了三种不同的TEP模型,尺寸不同,包括故障电流限制器和电阻控制的连续补偿装置.
- 基于融合速度,解决方案质量和可扩展性的评估算法.
主要成果:
- 开普勒优化算法 (KOA) 在所有测试的电力网络中在解决方案质量方面表现最好.
- 在某些案例研究中,KOA的平均值比第二好的算法低6.8%.
- 白优化器 (White Shark Optimizer,简称WSO) 呈现出更快的趋同 (2-3倍快),但平均解决值相对较高.
结论:
- 解决方案质量是传输扩展规划 (TEP) 优化中的至关重要的因素,取代了算法速度.
- 强烈建议使用开普勒优化算法 (KOA) 来实现最佳的TEP解决方案.
- 对TEP的元启发式算法性能进行进一步的研究是有必要的.
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