A New and Reliable Objective Functions for Extracting the Unknown Parameters of Solar Photovoltaic Cell Using Political Optimizer Algorithm

The primary issue in the design of photovoltaic (PV) systems is the availability of an exact PV model, and it emulates the PV cell behaviour. The precision of the PV model is based mainly on objective functions and optimization techniques. Based on this statement, this paper discusses two different objective functions to identify the unknown parameters of the solar cell using a novel Political Optimizer (PO) algorithm. The experimental values of the RTC France PV cell under 33°C and 1000 W/m
²
environmental condition are considered to identify the unknown parameters of the single-diode model (SDM) and double-diode model (DDM). The ohmic resistances are neglected in forming the first objective function to reduce the computation time, whereas, the ohmic resistance is considered in forming second objective function to increase the accuracy. The simulation results are presented for the PO algorithm, and the performance comparison is made among Grey Wolf Optimizer (GWO) and Harris Hawk Optimizer (HHO) with the PO algorithm. The PO algorithm confirms the accurate result and delivers an improved illustration for the PV cell experimental data.