Photovoltaic model parameters identification using an innovative optimization algorithm

Abstract

As it tackles electrical and non-electrical losses, the triple-diode model (TDM) of photovoltaic (PV) cells is highly exact. This paper employs a novel optimization method known as the innovative optimization algorithm (INFO) technique to correctly estimate the electrical characteristics of such TDM. To shift agents towards a better position, the INFO algorithm exploits the concept of weighted mean. The primary goal of INFO is to stress its performance features to solve some optimization difficulties that other approaches cannot effectively solve. In this paper, the objective function based on a combination of the absolute value of the current error, its squared value, and its quadrable value is employed, which the INFO optimizer minimizes to predict the optimum parameters of such TDM precisely. The proposed INFO algorithm is carried out on multi- and mono-crystalline varieties, such as the Kyocera KC200GT and the Canadian Solar CS6K-280 M. The simulation outcomes demonstrate the INFO's ability to extract the model parameters precisely. The INFO achieved the lowest ideal fitness values of 9.0738 × 10−06 and 5.7356 × 10−05 for the KC200GT and Canadian Solar CS6K-280 M, respectively, throughout the optimization procedure. Under various environmental circumstances, experimental validation of the calculated parameters using the (INFO) optimizer is carried out, and the results are compared to the observed values from the laboratory experiments. The simulation results demonstrate the INFO's convergence time and accuracy advantage over competing optimization techniques. Additionally, statistical analysis shows that the INFO optimizer is resilient.