Comparative performance analysis of metaheuristic search algorithms in parameter extraction for various solar cell models

Abstract

In today's technology, solar energy continues to be one of the leading renewable energy sources in electricity production methods due to its abundance on the Earth's surface and its non-polluting nature. In solar photovoltaic (PV) systems, it is relatively easy to produce electricity from the sun, and their efficiency can be enhanced by accurately estimating the electrical parameters of solar cells. From this point of view, this paper has focused on solar cell design to improve the performance of solar PV systems. To achieve this target, fitness-distance balance-based stochastic fractal search (FDB-SFS), particle swarm optimization (PSO), student psychology-based optimization (SPBO), and adaptive guided differential evolution (AGDE) algorithms are employed. The experimental analysis is performed on single-diode solar cell, double-diode solar cell, and three PV modules. Considering the experimental results, it is observed that the best estimation accuracy is reached by the FDB-SFS algorithm. Accordingly, root mean square error (RMSE) values between measured and estimated data were calculated to be 9.86E-04 for the single-diode solar cell model, 9.84E-04 for the double-diode solar cell model, 2.42E-03 for the Photowatt-PWP201 module, 1.72E-03 for STM6–40/36 module, and 1.67E-02 for STP6–120/36 module. Consequently, the present paper reports that FDB-SFS is an efficient and powerful method for parameter extraction of solar photovoltaic models.