增强的鱼优化算法用于太阳能光伏电池模型的参数识别:一项比较研究
Sha Yang1, Guojiang Xiong2,3, Xiaofan Fu1
1College of Electrical Engineering, Guizhou University, Guiyang, 550025, China.
Scientific reports
|July 21, 2024
概括
本研究比较了鱼优化算法 (WOA) 和其用于太阳能光伏 (PV) 模型参数识别的变体. 增强的WOA (EWOA) 在PV模型参数估计的准确性和稳定性方面表现出卓越的性能.
科学领域:
- 可再生能源工程可再生能源工程
- 计算智能是一种计算智能.
- 优化算法 优化算法
背景情况:
- 精确的太阳能光伏 (PV) 模型参数识别对于有效的光伏系统设计和性能分析至关重要.
- 太阳能电池的非线性电流电压特性对参数估计提出了重大挑战.
- 鱼优化算法 (WOA) 和其变体为解决复杂的优化问题提供了有希望的方法,例如PV参数识别.
研究的目的:
- 对标准鱼优化算法 (WOA) 和十种增强的WOA变体进行全面的比较分析.
- 评估这些算法在确定太阳能光伏模型的五个关键参数方面的性能.
- 确定最有效的WOA变种来解决难以解决的光伏参数识别问题.
主要方法:
- 应用和比较WOA和十个增强的WOA变体,以确定五个光伏模型参数.
- 使用评估指数,如解决方案准确性,搜索稳定性和融合曲线来评估算法性能.
- 采用多模型统计分析,包括在0.05信心级别的弗里德曼测试,来对算法进行排名.
主要成果:
- 增强的鱼优化算法 (EWOA),它集成了基于排序的差异突变运算机和莱维飞行策略,获得了最高排名.
- 与其他经过测试的WOA变体相比,EWOA在解决方案准确性和搜索稳定性方面表现出卓越的性能.
- 统计分析证实了评估算法的显著性能差异.
结论:
- 在研究的WOA变体中,EWOA被确定为太阳能光伏模型参数识别中最有效的算法.
- 该研究提供了对不同WOA增强器对于这个特定问题的性能差异的见解.
- 提出了未来研究方向的建议,以进一步改进复杂优化任务的WOA.
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