一个基于改进的海洋捕食者算法和平衡优化器的新型混合算法,用于太阳能光伏模型的参数提取
Ziyuan Liang1, Zhenlei Wang2,1, Ali Wagdy Mohamed3
1Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China.
Heliyon
|October 17, 2024
概括
一个新的混合算法,IMPAEO,增强了太阳能光伏 (PV) 模型参数提取. 这种方法提高了光伏系统模拟的准确性和可靠性,为研究人员提供了更好的替代方案.
科学领域:
- 可再生能源系统可再生能源系统
- 计算智能是一种计算智能.
- 电气工程 电气工程
背景情况:
- 精确的参数提取对于太阳能光伏 (PV) 系统的模拟和控制至关重要.
- 现有的优化算法往往缺乏用于PV模型参数提取所需的准确性和可靠性.
- 当前方法的缺陷需要开发更强大,更有效的参数提取技术.
研究的目的:
- 提出一种新的混合优化算法,IMPAEO,用于太阳能光伏系统中增强的参数提取.
- 为了提高光伏模型的参数提取的准确性,可靠性和收速度.
- 为现有光伏系统分析和评估方法提供更有效的替代方案.
主要方法:
- 通过整合精英的基于对立的学习,自适应的权重系数和EO操作员,开发了带有平衡优化器 (IMPAEO) 的改进海洋捕食者算法.
- 整合了线性人口大小减少 (LPSR) 技术,以动态优化人口大小并提高搜索性能.
- 使用CEC2017基准测试套件验证了IMPAEO算法,并将其应用于提取单二极管,双二极管和各种光伏模块的参数.
主要成果:
- 与其他算法相比,IMPAEO算法在解决方案质量和融合速度方面表现优越.
- 对比分析证实了IMPAEO在PV模型参数提取算法稳定性方面的显著优势.
- 对基准函数和各种光伏模型的验证证实了拟议的IMPAEO的有效性和可靠性.
结论:
- 拟议的IMPAEO算法是太阳能光伏模型中参数提取的高效可靠方法.
- 对于研究人员和工程师来说,IMPAEO提供了一个有前途的替代方案,他们致力于太阳能光伏系统.
- 混合方法有效平衡全球勘探和当地开发,从而提高解决方案质量和更快的融合.
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