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混合年轻的双裂纹实验和差异演变用于增强的光伏参数估计

Basma S Alqadi¹, Hassan Al-Sukhni², Diaa Salama AbdElminaam^3,4

¹Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia.

Scientific reports

|October 21, 2025

概括

一种新的Young's双实验-差异进化 (YDSE-DE) 方法提高了光伏 (PV) 模型的准确性. 这种混合方法增强了太阳能系统模拟的性能,优于其他优化技术.

科学领域:

可再生能源技术可再生能源技术
计算智能是一种计算智能.
光学是什么?光学是什么?光学是什么?

背景情况:

精确的光伏 (PV) 系统建模对于高效的太阳能集成至关重要.
现有的参数估计方法可能缺乏复杂的光伏模型所需的精度.
混合优化技术为科学建模中的性能提升提供了潜力.

研究的目的:

介绍和评估一种新的混合优化方法,即的双实验-差异进化 (YDSE-DE) 技术.
为了提高单元,双元和三元二极管光伏模型中的参数估计的精度.
为了证明YDSE-DE算法的优越性超过现有的优化方法在PV建模.

主要方法:

整合了的双实验 (YDSE) 原则与差异进化 (DE) 算法.
对YDSE-DE算法的比较评估与狮优化器 (ALO) 和Sooty Tern优化算法 (STOA).
在单二极管,双二极管和三二极管光伏模型中进行严格的测试和验证.

主要成果:

与ALO和STOA相比,YDSE-DE算法实现了更高的准确性和计算效率.
在所有测试的光伏模型中,观察到根平均平方误差 (RMSE) 的显著改善.
具体的RMSE值为:0.0059117 (单二极管),0.0015218 (双二极管),以及0.0018409 (三二极管). 这些值均为0.0059117 (单二极管),0.0015218 (双二极管) 和0.0018409 (三二极管).

结论:

YDSE-DE算法在光伏参数估计方面取得了重大进展.
这种混合方法提高了太阳能系统模拟和设计的精度和有效性.
该方法为优化需要可靠模拟的其他复杂系统提供了潜力.

关键词:

双倍二极管模型模型预测光伏参数的估计单个二极管模型太阳能是太阳能中的一种.三个二极管模型模型扬斯的双裂实验

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