在光伏系统中使用混合蝙蝠和的元启发算法进行参数估计.
Abdulsalam Ashour Mohameed Almabrouk1, Selçuk Alparslan Avci1, Javad Rahebi2
1Department of Electrical & Electronics Engineering, Karabuk University, Karabuk, Turkey.
Scientific reports
|January 6, 2026
概括
一种新的混合蝙蝠搜索算法优化了光伏 (PV) 电池参数估计,以提高太阳能性能. 这种方法提高了单,双和三二极管模型的准确性,优于现有技术.
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:
- 太阳能采用率正在上升,推动对高效光伏 (PV) 电池的需求.
- 太阳能电池的性能对辐射和温度敏感,需要准确的参数估计.
- 精确的参数估计是有效的最大功率点 (MPP) 追踪的关键.
研究的目的:
- 开发一种混合的元启发算法,以优化PV参数估计.
- 将全球搜索的蝙蝠算法 (BA) 与本地搜索的乌搜索算法 (CR) 结合起来.
- 为了提高光伏模型参数识别的准确性和稳定性.
主要方法:
- 一个混合的元启发算法,整合了蝙蝠算法 (BA) 和乌搜索算法 (CR).
- 对于单二极管 (SDM),双二极管 (DDM) 和三极管 (TDM) 模型的应用.
- 使用法国RTC数据集进行验证,并与粒子群优化 (PSO) 和遗传算法 (GA) 进行比较.
主要成果:
- 混合BA-CR算法表现出卓越的融合和稳定性.
- 实现了较低的根平均平方误差 (RMSE) 值: 0.00077299 (SDM), 0.0008215 (DDM), 0.0008068 (TDM). 它们可以分别为:
- 与传统方法相比,证明有效地管理参数不确定性.
结论:
- 拟议的混合BA-CR算法为光伏参数估计提供了显著的进步.
- 这种方法提高了太阳能电池建模的准确性,以提高能源产量.
- 该方法为优化光伏系统性能提供了强大的解决方案.
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