Parameter Estimation of Photovoltaic based on Chaotic Elite Mountain Gazelle Optimizer

This research presents a technique for optimizing photovoltaic (PV) characteristics using a modified version of the Mountain Gazelle Optimizer (MGO). The method under consideration is referred to as CEMGO. The Mountain Gazelle Optimizer (MGO) is a meta-heuristic algorithm that draws inspiration from the social structure and hierarchy observed in wild mountain deer. This paper utilizes CEMGO to ascertain the parameters of photovoltaic solar panels using a single diode model, relying on experimental datasets. To verify the effectiveness of the CEMGO approach. This article employs the original MGO algorithm for the sake of comparison. The comparison function utilized is the root mean square error. Based on the simulation findings, the CEMGO value outperforms the MGO approach, with a superiority of 23.07%.