Parameters Estimation of PV Modules for a Three-Diode Model Using an Enhanced Salp Swarm Algorithm

The internal parameters for solar cells may change over a period of operation, which must be re-estimated to produce power output close to the actual value. This paper proposes an enhanced salp swarm algorithm (ESSA) combined with differential evolution (DE) to optimize the internal parameters for a three-diode power generation model. The three-diode model is accurate but more complicated than single-and two-diode models. To accurately estimate the internal parameters, an ESSA is used and an index for dynamic crowing distance (DCD) is applied to confirm whether DE is performed. The proposed method is verified using a 200 kWp PV power generation system. Three different diode models that are optimized using different optimization algorithms are compared for different weather conditions. The results show that the three-diode model gives more accurate estimation than single-and two-diode models. Furthermore, the proposed ESSA allows more accurate estimation than the parameter estimation results using the other two benchmark algorithms.