通过增强的母树方法精确提取三二极管光伏系统中的电气参数:一种用于参数估计的新方法
Mouncef El Marghichi1, Abdelilah Hilali2, Abdelkhalek Chellakhi3
1Intelligent Systems Design Laboratory (ISD), Faculty of Science, Abdelmalek Essaadi University, Tetouan, Morocco.
PloS one
|March 4, 2025
概括
本研究介绍了与气候变化相关的母树优化 (MTO-CL) 算法,用于精确的太阳能光伏 (PV) 模块参数提取. MTO-CL显著提高了光伏系统建模的估计准确性和可靠性.
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.
- 材料科学 材料科学 材料科学
背景情况:
- 精确模拟光伏 (PV) 模块对于太阳能应用至关重要.
- 对光伏系统的参数提取是具有挑战性的,因为它们固有的非线性特征.
- 现有的优化方法可能会与光伏模块建模的复杂性作斗争.
研究的目的:
- 引入和评估与气候变化 (MTO-CL) 母树优化算法.
- 为了提高太阳能光伏三极管模型的参数估计.
- 为了提高光伏系统模拟的准确性和可靠性.
主要方法:
- 开发MTO-CL算法,结合气候变化启发的解决方案更新和调整的适应.
- 在不同的辐射和温度条件下,MTO-CL的应用用于ST40和SM55太阳能模块的参数估计.
- 与七种替代优化方法进行比较分析.
主要成果:
- 与七种替代方法相比,MTO-CL在参数估计方面表现优越.
- 实现了非常低的根平均平方误差 (RMSE) 值 (0.0025A至0.0165A) 和平均平方误差 (MSE) 值 (6.2 × 10^-6至2.7 × 10^-4).
- 显著降低功率误差,范围从22.86mW到239.40mW,表明高精度.
结论:
- MTO-CL算法有效地提高了太阳能光伏三极管模型的参数估计.
- MTO-CL为改善光伏系统建模和太阳能应用提供了强大而准确的工具.
- 算法的勘探和开发之间的动态平衡确保有效识别最佳参数.
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