Enhanced Artificial Rabbits Algorithm Integrating Equilibrium Pool to Support PV Power Estimation via Module Parameter Identification

Abstract

This paper proposes a novel innovative version of enhanced artificial rabbit optimization (EARO) algorithm integrating an equilibrium pool (EP) that consists of the best solutions. Furthermore, the detour foraging and hiding mechanisms are modified to amplify the search capability. These modifications enable the algorithm to dynamically focus on exploring various randomized directions emanating from the EP. The proposed EARO is designed to investigate the PV module characteristics identification issue. To obtain the nine parameters of the PV triple diode model (TDM) while taking into account three distinct real‐world PV modules, the novel version of EARO is utilized and evaluated in comparison with to the standard ARO. The novel EARO is tested on three different PV modules: the Ultra 85‐P PV panel, the PVM_752GaAs, and the RTC France. The results corresponding to the novel EARO are compared with respect to several published latest studies. The results of the simulation show that the proposed EARO shows significant overall improvement rates for each of the three modules. A validation of the novel EARO on the common SDM and DDM of the RTC France PV is assessed which illustrates a significant superiority and robustness of the novel EARO over recent published results.