一种混合气味剂共生生物搜索算法,用于最佳控制微电网运行
Salisu Mohammed1, Yusuf A Sha'aban2,3,4, Ime J Umoh2
1Department of Maintenance Engineering, Nigerian National Petroleum Company, KRPC Limited, Kaduna, Nigeria.
PloS one
|June 7, 2023
概括
一个新的混合算法,气味代理共生生物体搜索 (SASOS),增强了自主微电网控制. SASOS 提高了电力系统的稳定性,并比现有的方法更有效地减少了总波扭曲 (THD).
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 优化算法 优化算法
背景情况:
- 自主微电网需要强大的控制来稳定频率和电压.
- 现有的单一优化算法在微电网控制中难以平衡精度和速度.
- 混合算法可以改善开发-勘探平衡,以提高控制效率.
研究的目的:
- 开发和评估混合气味剂共生生物搜索 (SASOS) 算法,以最佳控制自主微电网.
- 加强能源资源的协调,以实现最佳的发电和负载分配.
- 考虑网络功率流和离散时间控制参数来制定优化问题.
主要方法:
- 开发了一种混合SASOS算法,该算法结合了共生生物搜索 (SOS) 和气味代理优化 (SAO).
- 在24个标准测试功能基准测试中验证了SASOS性能.
- 在微电网中央控制器 (MCC) 中实现SASOS,用于模拟研究.
主要成果:
- 在17个基准函数中,SASOS实现了58.82%的预期收目标 (DCG).
- 与SOS (15.60%),SAO (12.74%) 和MCC (6.04%) 相比,在微电网负载干扰排斥方面显示出更高的总波扭曲 (THD) 减少 (19.76%).
结论:
- 与标准的SOS,SAO和MCC方法相比,SASOS在自主微电网控制方面表现出卓越的性能.
- 混合算法为增强微电网控制系统提供了一个有希望的解决方案.
- 在其他工程优化领域,SASOS显示出潜在的适用性.
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