Abstract
Solar photovoltaic (PV) energy systems are representing the most attractive source of energy, especially with the continuous reduction of manufacturing costs. The new systems' sizing and cost estimation depend mainly on the accurate modeling of the PV cells. The modeling of the PV cells is based on one-diode modeling with five unknown parameters or the more accurate modeling based on two-diode modeling that have seven unknown parameters, or with a higher number of shunt diodes modeling with a higher number of unknown parameters. An accurate and fast estimation of the PV cell parameters will help designers to build their decision based on accurate results, where any small variation of these parameters can spoil the results obtained from the modeling and can produce an unwise decision. A high number of studies are introduced in the literature to estimate these parameters. These studies are different in the accuracy of results and the time consumed to get these results. Some of these studies used metaheuristic algorithms which are characterized by slow convergence and may cause inaccurate results. For this reason, a recent optimization algorithm called the musical chairs algorithm (MCA) is introduced in this paper to estimate these parameters faster and more accurately than many metaheuristic algorithms. The idea behind the use of MCA is to have a high number of search agents in the beginning to enhance the exploration and continuously reduce this number to enhance exploitation at the end of optimization and reduce the convergence time. The results obtained from using 10 optimization algorithms showed that the error associated with MCA is 20% of the average error of the other optimization algorithms. Moreover, the MCA never misses the global minimum of error which was not the case in other optimization algorithms. The MCA obtained these results in 40% of the time consumed by other optimization algorithms. These results of the MCA showed its superiority compared to other optimization algorithms.