This paper aims to develop a robust and practical photovoltaic (PV) Maximum Power Point (MPP) identification tool developed using reliable experimental data sets. The correlations between the voltage and the current (V_mp and I_mp) at maximum power from one side, and the irradiance information, electrical parameters, thermal parameters and weather parameters from another side, are investigated and compared. A comparative study between a number of input scenarios is conducted to minimize the MPP estimation error. Four scenarios based on a combination of various PV parameters using various Artificial Neural Network (ANN)-based MPP identifiers are presented, evaluated using the most common regression measure (Mean Squared Error (MSE)), improved in terms of the accuracy of the identification of MPP, and then compared. The first scenario is divided into two parts I(a) and I(b) and considers the irradiance information in addition to the highest correlated parameters with I_mp and V_mp, which are circuit current (I_sc) and open-circuit voltage (V_oc), respectively. The second scenario considers irradiance information and the electrical parameters only. The irradiance information, in addition to the electrical, thermal, and weather parameters, are considered in the third scenario using a single layer network, while the irradiance information, in addition to the electrical, thermal, and weather parameters, are considered in the fourth scenario using a two-layer ANN network. Although the correlation study shows that the V_mp and I_mp have the best correlation with the open-circuit voltage and the short circuit current (scenario I), respectively. Nonetheless, the consideration of irradiance, electrical, thermal, and weather parameters (scenario IV) yielded higher identification accuracy. The results showed a decrease in the MSE of V_mp by 74.3% (from 1.6 V to 0.411 V), and in the MSE of I_mp by 95% (from 4.4e−6 A to 2.16e−7 A), respectively. In comparison to the conventional methods, the proposed concept outperforms their performances and dynamic responses. Moreover, it has the potential to eliminate the oscillations around the MPP in cloudy days. The MPP prediction performance is 99.6%, and the dynamic response is 276 ms.

本文旨在开发使用可靠的实验数据集开发的强大实用的光伏（PV）最大功率点（MPP）识别工具。电压和电流之间的相关性（V _mp和I _mp）以一侧的最大功率进行测量，并比较另一侧的辐照度信息，电气参数，热参数和天气参数。为了最大程度地减少MPP估计误差，在许多输入方案之间进行了比较研究。提出了四种方案，这些方案基于使用各种基于人工神经网络（ANN）的MPP标识符的各种PV参数的组合，并使用最常见的回归度量（均方误差（MSE））进行了评估，从而提高了识别的准确性MPP，然后进行比较。第一种情况分为两个部分I（a）和I（b），除了_Imp和V _mp的最高相关参数外，还考虑辐照信息，它们是电路电流（I_sc）和开路电压（V _oc）。第二种情况仅考虑辐照度信息和电气参数。在第三种情况下，使用单层网络考虑了除电，热和天气参数之外的辐照度信息，而在第四种情况下考虑了除电，热和天气参数外的辐照度信息使用两层ANN网络的场景。尽管相关研究表明V _mp和I _mp分别与开路电压和短路电流具有最佳相关性（方案I）。尽管如此，考虑到辐照度，电，热和天气参数（方案IV）仍可以提高识别精度。结果表明，V _mp的MSE降低了74.3％（从1.6 V降至0.411 V），I _mp的MSE降低了95％（从4.4e-6 A降低到2.16e-7A）。与常规方法相比，所提出的概念优于其性能和动态响应。此外，它有可能消除阴天MPP周围的振荡。MPP预测性能为99.6％，动态响应为276 ms。