Parameters extraction of photovoltaic cells using swarm intelligence-based optimization technique: research on single diode model and double diode model

Solar-energy is a clean source of energy and photovoltaic (PV) panels are constructed from solar cells (SC) which convert energy of light into electricity without any environmental effect. The researchers and policy makers focus on the huge scale adoption of solar panels due to their cleaner production. However, there is non-linear behavior in current-voltage characteristics of solar panels and shortage of data in manufacturer's datasheet. To enhance the efficiency of solar panels it is mandatory to develop the PV panels scheme accurately by extracting the basic parameters. In this research study a mathematical model of two different solar cell models is used such as Single Diode Model (SDM) and Double Diode Model (DDM). The Particle Swarm Optimization (PSO) is used to extract the five and seven unknown parameters of SDM and DDM. The algorithm runs with one thousand iterations to minimize the Root Mean Square Error (RMSE) where the RMSE is the vector of five unknown parameters for SDM(I-ph, I-s1, a(1), R-s, and R-sh) and seven (I-ph, I-s1, I-s2, a(1), a(2,) R-s, and R-sh) for DDM. The superiority of the proposed PSO algorithm is proved by the optimized results of unrevealed parameters with minimized RMSE of up to 10(-3). Optimum parameter values for the solar cell models are applied on the real time data of a 55 mm diameter commercial RTC-France SC. Finally, the results reveal that P-V and I-V curves exhibit the smallest deviation between estimated and real time values. The results reveal that the proposed PSO converges to optimal solution with least number of iterations compared to the existing metaheuristic algorithms.