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重新配置的单二极管和双二极管模型,用于改进太阳能电池/模块的建模

Emre Çelik¹, Mehmet Karayel², Dinçer Maden¹

¹Department of Electrical and Electronics Engineering, Engineering Faculty, Düzce University, Düzce, Türkiye, Turkey.

Scientific reports

|January 15, 2025

概括

本研究介绍了重新配置的太阳能光伏 (PV) 模型 (Reconfig-SDM和Reconfig-DDM) 以提高光伏系统效率. 这些新型号,通过松鼠搜索算法 (SSA) 进行优化,在光伏设备建模中表现出卓越的准确性.

科学领域:

可再生能源工程可再生能源工程
光伏设备建模光伏设备建模
计算智能是一种计算智能.

背景情况:

对光伏 (PV) 电池和模块进行准确的建模对于优化光伏系统效率至关重要.
现有的研究主要集中在单二极管模型 (SDM) 和双二极管模型 (DDM) 上,通常使用元启发算法进行参数提取.
经典模型的局限性需要更具代表性的方法来准确预测光伏性能.

研究的目的:

为增强光伏设备建模引入新型重新配置的单二极管 (Reconfig-SDM) 和双二极管 (Reconfig-DDM) 模型.
采用松鼠搜索算法 (SSA) 进行拟议模型的全局参数识别.
通过实验数据验证 Reconfig-SDM 和 Reconfig-DDM 的有效性,并与现有方法进行比较.

主要方法:

开发 Reconfig-SDM 和 Reconfig-DDM 通过将串联电阻纳入经典 SDM 和 DDM 的二极管.
应用松鼠搜索算法 (SSA) 来优化新提出的光伏模型的参数.
使用RTC法国商用太阳能电池和CS6P-220P多晶光伏模块进行实验验证.

主要成果:

建议的Reconfig-SDM和Reconfig-DDM,通过SSA进行优化,在建模光伏设备方面表现出更高的准确性和有效性.
与最好的文献方法相比,实现了显著的错误减少:太阳能电池高达0.37%,太阳能模块高达3.21%.
再配置-DDM显示,太阳能电池的错误减少了2.58%,太阳能模块的错误减少了29.0%.

结论:

与传统模型相比,新的 Reconfig-SDM 和 Reconfig-DDM 提供了更准确,更具代表性的光伏设备建模.
在这些先进的光伏模型中,松鼠搜索算法 (SSA) 是用于全球参数提取的有效工具.
拟议的建模方法为光伏系统效率和性能预测提供了显著的改进.

关键词:

具有I-V特征的人.太阳能光伏建模模型参数识别参数识别重新配置的DDM进行了重新配置.重新配置的SDM进行了重新配置.松鼠搜索算法松鼠搜索算法

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