一个新的袋鼠逃生优化器,用于通过一,二和三二极管等效电路建模来对太阳能光伏电池/模块进行参数估计
Sulaiman Z Almutairi1, Abdullah M Shaheen2
1Department of Electrical Engineering, College of Engineering, Prince Sattam bin Abdulaziz University, Al Kharj, 16278, Saudi Arabia.
Scientific reports
|September 23, 2025
概括
一个新的袋鼠逃生优化 (KEO) 算法准确地提取光伏 (PV) 模型的参数. 凯欧模拟了袋鼠的行为,以实现高效和强大的太阳能系统建模.
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:
- 精确的参数提取对于建模光伏 (PV) 系统至关重要.
- 现有的优化算法在处理光伏模型复杂的非线性动态方面面临着挑战.
- 受自然启发的算法为改进光伏参数识别提供了潜在的潜力.
研究的目的:
- 介绍一个新的元启发算法,袋鼠逃脱优化 (KEO),用于单,双和三倍二极管光伏模型的精确参数提取.
- 模拟袋鼠的生存驱动逃生行为,以指导优化过程.
- 为了评估KEO的性能与最先进的优化器及其适用于现实世界的光伏数据集.
主要方法:
- 开发了以袋鼠逃生行为为灵感的KEO算法,具有双相探索 (旋,长跃) 和自适应性利用.
- 整合了一个混乱的物流能源适应战略和一个诱下降机制,以实现精细的融合.
- 通过使用CEC 2022基准,信心区间分析,超参数灵敏度研究和来自RTC France和Photowatt-PWP-201光伏模块的现实世界I-V数据集来验证KEO.
主要成果:
- 与现有的光伏参数识别优化算法相比,KEO在准确性,稳定性和融合效率方面表现优异.
- 该算法有效地捕捉了太阳能电池的非线性动态,跨越各种等效电路模型.
- 在各种辐射和温度条件下,KEO显示出显著的性能,证实了其用于光伏系统建模的可靠性.
结论:
- 袋鼠逃生优化 (KEO) 是用于光伏 (PV) 参数提取的高效和强大的算法.
- 凯欧为可再生能源建模提供了一种通用而强大的工具,准确地表示复杂的光伏系统动态.
- 拟议的方法提高了太阳能系统分析和设计的可靠性和效率.
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