Modeling, Parameter Optimization, and Experimental Comparison of a PV Array with Daily Data in Outdoor Conditions

In photovoltaic (PV) systems, it is an important requirement to model the PV as close to reality as possible in situations such as installation, detection of system faults, detection of sensor errors, and monitoring of energy. For this purpose, current-voltage (I-V) characteristics are frequently used. However, doing this under current operating conditions is costly. In addition, the same brand-model PVs in a system may have differences due to production. These differences increase over the years with aging. Therefore, it is challenging to create a global model of the system with I-V characteristics. Based on this, this study proposes a method for estimating the global PV parameters of a PV array using daily data recorded while under operating conditions. The estimation of PV parameters is done using the Puma optimizer (PO) algorithm. The actual data from different days are compared with the estimated data to verify the proposed model. As a result of the comparison, the minimum $\mathbf{R}^{2}$ value is calculated as 0.9931. Thus, the success of the proposed method in estimating the global parameters of a PV system operating under operating conditions is verified.