LAUSR

Abstract The parameters of solar cells models have an effect on the simulation of solar cells and can be applied to monitor the working condition and diagnose potential faults for photovoltaic (PV) modules in a PV system. To accurately and efficiently extract the optimal parameters of solar cells in a limited CPU run time, a modified simplified swarm optimization (MSSO) algorithm is presented for the single diode and double diode models by minimizing the least square error between the calculated and experimental data. In MSSO, a new one-variable-update mechanism and survival-of-the-fittest policy are applied to enhance the ability of traditional SSO. To investigate the performance of MSSO, comparative studies with other well-known optimization algorithms, i.e., SSO, artificial bee colony (ABC) and simplified bird mating optimizer (SBMO), are presented, and extensive computational results are shown. The statistical data indicate that the MSSO method has the best performance among these methods in terms of efficiency, robustness and accuracy. Moreover, the current vs. voltage characteristics of the parameters extracted by MSSO coincide well with those of experimental data.