Estimation of photovoltaic model parameters using the Rao-1 optimization algorithm

Predicting the performance of photovoltaic (PV) systems in industrial applications has become increasingly important due to the complex and non-linear behavior of PV modules. Accurate performance prediction is essential for optimizing energy production and ensuring system reliability. However, a significant challenge arises from the lack of critical information in manufacturer datasheets, which often do not include all the necessary details for precise PV module modeling. This limitation can hinder accurate analysis and system performance evaluation. Optimization methods are recommended to accurately determine the electrical and physical characteristics of PV cells and modules to address this issue. These methods enable detailed and precise analysis of current-voltage (I-V) and power-voltage (P-V) characteristics across various PV models, improving the overall modeling accuracy. Among these methods, the Rao-1 optimization technique has been selected and evaluated in this paper to solve this issue. It is specifically used to extract the unknown parameters of photovoltaic models, including the photo-generated current (Iph), diode ideality factor (n), series resistance (Rs), reverse saturation current (I0), and shunt resistance (Rsh). The effectiveness of the Rao-1 optimization method has been demonstrated through its successful application in estimating PV parameters across a wide range of datasets, ensuring its reliability and adaptability for various industrial scenarios.