Optimal Parameter Estimation of Solar Cell using Simulated Annealing Inertia Weight Particle Swarm Optimization (SAIW-PSO)

The operation of Photovoltaic (PV) system mainly rely on appropriate modeling of solar cells and optimum approximation of parameters associated with them. Recently, various hybrid, numerical and analytical techniques were proposed to extract optimal parameters of PV cell. This paper presents an efficient approach, A Simulated Annealing Inertia Weight Particle Swarm Optimization (SAIW-PSO) for optimal estimation of PV parameters for double and single diode models. In addition, fitness indicator is guided using the Newton Raphson Method (NRM) that supports SAIW-PSO to explore the optimal solution. The premature convergence problem of typical PSO is resolved by the proposed framework. The strength of proposed approach is validated under standard test conditions (STC) on RTC France Silicon Solar cell. The SAIW-PSO is capable to explore optimum solution in smaller number of iterations and less computation time. The obtained results clearly depict that the proposed framework is fast, efficient and much accurate for PV cells parameters approximation.