An improved PSO-based approach for the photovoltaic cell parameters identification in a single diode model

The future power of photovoltaic systems (PVS) is gaining significant attention due to its rising potential. This has resulted in a substantial amount of research emphasizing the importance of optimizing the PVS efficiency. However, the identification of PV cell model parameters remains a challenging task, mainly due to the characteristics of PV cells and their dependence on varying meteorological conditions. In this work, we present a novel methodology based on an improved new multi objective particle swarm optimization (NMOPSO) algorithm for the PV cell parameters identification. The main goal is to minimize the root mean square error (RMSE) and to calculate the series resistance (Rs) by means of its non-linear equation form. The applied algorithm uses an evolving and adaptive search strategy to enhance both speed of convergence for the parameter identification process precision. Through extensive simulations, we demonstrate that proposed approach outperforms current methods in terms of accuracy, precision, and PV parameters extraction.