Parameter Extraction of SolarPhoto Voltaic Module by Using IGWO

The three main I-V characteristic data points are provided by the makers of photovoltaic (PV) panels. To obtain the five parameter model, or single diode model, however, requires more information than this alone. Thus, the literature proposes a number of population-based Meta heuristic approaches. To extract parameters of diode model of PV panels from the datasheet, an algorithm that is well-balanced is necessary. This research proposes a revolutionary IGWO algorithm for solar cell parameter extraction. As a result of the depletion of vestige fuels and carbon emissions that contribute to climate change and global warming, power producers are shifting their attention from traditional energy sources to sustainable ones. The usage of solar cells is the most widely accepted remedy for these problems. Prior to installation, a precise parameter assessment of the solar cells is essential to achieve optimal performance. Recent research has examined the applications of several optimization algorithms for the estimate of solar cell parameters. A more advanced version of Grey Wolf Optimizer (GWO) called Intelligent Grey Wolf Optimizer (IGWO) employs a sinusoidal curtailed function as a bridging method and conflict based learning. It was just released. The wide applicability of this variation has been assessed with multiple real-world problems and a range of conventional benchmark functions.