Parameter Estimation of Different Photovoltaic Models Using Hybrid Particle Swarm Optimization and Gravitational Search Algorithm

The performance of a typical solar energy-based system can be improved by accurately modeling the current versus voltage characteristics involved cells. However, estimating exact value parameters related to cells is quite challenging. optimization function, considering current–voltage cells, requires solution sophisticated non-linear and multi-modal methods. So far, various approaches have been reported. This paper proposes application new hybrid algorithm, i.e., Particle Swarm Optimization Gravitational Search Algorithm (PSOGSA), which combination two algorithms, (GSA) (PSO) method. PSOGSA algorithm superior other algorithms in terms higher accuracy searching for optimal solutions better explorative capability. Moreover, developed benchmarked using ten standard test functions verify its efficiency. In this manuscript, monocrystalline polycrystalline are considered. parameter results obtained further compared with those presented literature, such as PSO, GSA, MVO, HBO, PO SCA. complete error analysis carried out modified single-diode model (MSDM), double-diode (MDDM), three-diode (MTDM) photovoltaic (PV) prove superiority PSOGSA. statistical based on Friedman’s ranking Wilcoxon’s rank sum test. comparison show that proposed than unknown PV parameters.