modified social network search algorithm combined with halley’s method for parameter estimation in a photovoltaic cell, module and array

A significant research focus is how to make photovoltaic (pv) systems operate as efficiently as possible. a sufficient, accurate, and detailed model of the actual pv system is needed in order to get the best performance out of solar panels. more specifically, the parameters of these models are fitted to actual data to determine the correctness of the models. in order to determine the most accurate parameters of a photovoltaic cell, module, and array using actual data, this research suggests a novel method called msns-hal, which combines halley’s method with a modified social network search algorithm. a control parameter with a gaussian and cauchy distribution is randomly added to the search space to improve parameter estimation performance and speed up the agents’ convergence to the best solution. the best estimate of currents is then determined using halley’s root-finding technique. the proposed model, which has a best root mean square error of 7.1719 x 10^-4 for the rtc cell, 2.0388 x 10^-3 for the photowatt pwp module, and 0.0069 for the experimental field of 18 pv panels, has the highest accuracy when compared to 12 other current optimization approaches.