Hybrid ACOR PSO Approach for The Identification of Photovoltaic Panel Parameters

This paper proposes a hybrid optimization approach combining continuous domain ant colony optimization (ACOR) and particular swarm optimization (PSO) algorithms for the identification of photovoltaic panel parameters. The proposed ACOR-PSO method exploits the exploitation capacity of ACOR to generate relevant candidate solutions, while the swarm optimization (PSO) algorithm intervenes in the local exploitation phase to refine the search for the best solutions. This collaboration overcomes the constraints specific to each technique, including blocking or anticipated convergence. The main objective is to optimize the accuracy, consistency and convergence speed in the estimation of the five essential parameters of the diode model (Iph, Is, Rs, RSH, a). Validation was carried out on Matlab using experimental data collected in the field under standard environmental conditions. The results indicate that the hybrid ACOR PSO approach provides more accurate identification, increased stability, and acceptable convergence time compared to conventional methods, achieving a root mean square error (RMSE) of 1 × 10⁻³ and stable parameter estimation over multiple runs. Despite its higher computational cost, the proposed approach demonstrates robustness and reliability in identifying PV parameters, making it promising for modeling, simulation, control, and energy optimization applications in the photovoltaic sector.