光伏电池组件隐式、显式单二极管模型准确性对比研究

发布时间：2018-05-29 20:05

本文选题：光伏电池组件 + 单二极管模型　；参考：《物理学报》2014年17期

【摘要】：光伏电池组件非线性输出特性的物理建模及其优化参数的准确提取是光伏发电系统设计计算、性能评估及优化控制的重要前提.相对于传统的隐式单二极管模型,该文在光伏电池显式单二极管模型的基础上利用Lambert W函数推导了光伏组件的显式单二极管模型,提出一种基于重启边界约束Nelder-Mead单纯形算法的参数提取方法rbcNM,并利用两种典型光伏电池组件的实测数据对隐式、显式单二极管模型的准确性进行了对比测试和验证.结果表明:rbcNM算法可以快速准确的提取隐式、显式单二极管模型的优化参数,计算结果与实测数据具有很好的一致性,相对于已有文献在准确度上取得了大幅度的提升;显式单二极管模型的准确性显著高于隐式单二极管模型,对光伏电池组件的电流-电压和功率-电压特性曲线具有更高的拟合精度.
[Abstract]:The physical modeling of the nonlinear output characteristics of the photovoltaic cell components and the accurate extraction of the optimized parameters are the important precondition for the design and calculation of the photovoltaic power system, the performance evaluation and the optimization control. Compared with the traditional implicit single diode model, this paper derives the light by using the Lambert W function on the basis of the explicit single diode mode of the photovoltaic cells. An explicit single diode model of the volt component is presented, and a parameter extraction method based on the restart boundary constraint Nelder-Mead simplex algorithm (rbcNM) is proposed. The accuracy of implicit and explicit single diode model is tested and verified by using the measured data of two typical photovoltaic cell components. The results show that the rbcNM algorithm can be fast and accurate. The optimal parameters of the explicit single diode model are extracted and the calculated results are in good agreement with the measured data. The accuracy of the explicit single diode model is significantly higher than the existing literature. The accuracy of the explicit single diode model is significantly higher than that of the implicit single diode model, and the current voltage and power voltage of the photovoltaic cell components are higher than those of the single diode model. The characteristic curve has higher fitting precision.
【作者单位】：河南农业大学农业部农村可再生能源新材料与装备重点实验室;郑州大学信息工程学院;
【基金】：河南省重点科技攻关项目(批准号:102102210154) 教育部博士点专项科研基金项目(批准号:20104105110004) 国家自然科学基金(批准号:U1204607)资助的课题~~
【分类号】：TM914.4

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