An Examination of The PSO Algorithm’s Sensitivity For Estimating Solar Cell Parameters

Extracting parameters from photovoltaic cells poses a complex optimization challenge due to its inherent nonlinearity. This study investigates the sensitivity of the Particle Swarm Optimization (PSO) algorithm in estimating solar cell parameters. The evaluation assesses PSO’s exploitation and exploration capabilities, factoring in measurement errors. Performance is compared across six measurement error scenarios using metrics like convergence rate along with root mean square error (RMSE). It is very evident from the results that for case 2, the PSO algorithm achieves the lowest RMSE value of 0.02980, while it produces nearly identical RMSE values of 0.05040 and 0.04915 for cases 1 and 6. Simulation results clearly indicate that the PSO algorithm effectively handles measurement inaccuracies of 1% and 3%.