An error criterion is essential in the process of parameter extraction of photovoltaic (PV) modules by fitting I–V curves, which exerts a huge influence on the accuracy of the extracted parameters. This paper proposes a new integrated current–voltage error criterion, named EC-I&V(x), which takes into account the intrinsic I–V properties of the PV module. The deviation in both current and voltage is considered by combining the mean squared error of the current and voltage in different data regions. Four optimization methods are used to validate the proposed error criterion, including guaranteed convergence particle swarm optimization, differential evolution, shuffled complex evolution, and an artificial bee colony algorithm. Different methods with the proposed error criterion are applied to synthetic I–V curves with variable error levels and measured I–V data under different operating conditions. Comparing with the traditional current based error criterion, more accurate results are obtained by using the proposed EC-I&V(x) at different error levels for different optimization methods. The proposed EC-I&V(x) not only improves the accuracy of each extracted parameter but also improves the accuracy of the estimated I–V property near maximum power points.

中文翻译：

---

基于电流-电压误差综合判据的光伏单二极管模型参数提取

在通过拟合 I-V 曲线来提取光伏 (PV) 组件参数的过程中，误差准则是必不可少的，这对提取参数的准确性产生巨大影响。本文提出了一种新的集成电流-电压误差准则，称为 EC-I&V(x)，它考虑了 PV 模块的固有 I-V 特性。通过结合不同数据区域的电流和电压的均方误差来考虑电流和电压的偏差。四种优化方法用于验证所提出的误差准则，包括保证收敛粒子群优化、差分进化、混洗复进化和人工蜂群算法。将采用所提出的误差标准的不同方法应用于具有可变误差水平的合成 I-V 曲线和不同操作条件下测得的 I-V 数据。与传统的基于电流的误差准则相比，通过使用所提出的 EC-I&V(x) 在不同的误差水平下针对不同的优化方法获得更准确的结果。所提出的 EC-I&V(x) 不仅提高了每个提取参数的准确性，而且还提高了最大功率点附近估计 I-V 属性的准确性。