使用星优化算法对光伏电池/模块模型进行参数提取,采用基于secant的客观功能的修改算法.
1Department of Electrical Engineering, University of Science and Technology Houari Boumediene, P.O. Box 32, El-Alia, Algiers, 16111, Algeria. ybouali@usthb.dz.
本研究引入了一种新的基于切割的方法,以提高光伏 (PV) 参数提取的准确性. 星优化算法与secant方法显著提高PV模型性能预测.
科学领域:
- 可再生能源可再生能源是可再生能源.
- 电气工程 电气工程
- 计算优化计算优化
背景情况:
- 精确的光伏 (PV) 参数识别对于可靠的电气建模和能源产量预测至关重要.
- 当前的方法往往专注于算法,忽视了客观函数配方改进.
- 根平均平方误差 (RMSE) 是用于最小化测量和估计的光伏特征之间的差异的常规度量.
研究的目的:
- 为了评估SFOA (海星优化算法) 的PV参数提取.
- 为提高准确性,提出和验证基于secant的新型目标函数重构.
- 将拟议的方法与各种光伏模型中的现有优化算法进行比较.
主要方法:
- 使用星优化算法 (SFOA) 来进行参数提取.
- 引入一个基于secant的对象函数的重构.
- 验证单二极管 (SDM),双二极管 (DDM) 和三二极管 (TDM) 模型以及光伏模块 (PVM) 的框架.
- 使用RTC法国和Photowatt-PWP201基准数据集进行实验验证.
主要成果:
- 在所有测试的光伏模型 (SDM,DDM,TDM,PVM) 中,SFOA-Secant配置实现了卓越的准确性.
- 通过整合基于secant的目标函数,观察到估计准确度和稳定性的显著提高.
- 拟议的方法在最小化RMSE值方面超过了竞争中的优化算法.
结论:
- 使用secant方法重新制定目标函数是改善PV参数提取的有效策略.
- SFOA-Secant方法为光伏电气建模和性能评估提供了更高的准确性和稳定性.
- 这项工作强调了客观函数设计在光伏参数识别的优化问题中的重要性.
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