Mountain Gazelle Optimiser-based single, double, and triple diode models associated solar cells and panels parameters extraction

Precisely estimating unidentified variables in photovoltaic (PV) cells/panels models is imperative to accurately evaluate solar cells/panels conversion performance to assist the sustainability, technology, and business aspects of societal benefits. With this motivation, in this paper, the Mountain Gazelle Optimiser (MGO) metaheuristic algorithm is proposed for the first time to discern the parameters of the various four standard benchmark photovoltaic cell/panel data sets, namely PV cell: amorphous silicon and monocrystalline silicon and PV panels: Sharp ND-R250A5, and PVM 752 GaAs. Evaluated performance analysis with root mean squared error (RMSE) using the prominently considered photovoltaic cell/panel models associated with the SDM (single diode model), DDM (Double Diode Model) and TDM (Triple Diode Model). This paper examines the subsequent performance effectiveness of the Mountain Gazelle Optimiser (MGO) for the Photovoltaic (PV) system application to identify five parameters (SDM), seven parameters (DDM), and nine parameters (TDM) in comparison with standard deterministic methods and other considered metaheuristic optimisation algorithms. According to the evaluation of the MGO performance in two solar cells/panels, extraction of SDM and DDM model parameters, MGO achieves excellent optimal parameters with minimal root mean square error (RMSE) of the amorphous silicon PV cell (SDM: 4.612322E−05, DDM: 4.094400E−05and TDM: 4.094395E−05), monocrystalline silicon cell (SDM: 5.630971E−0, DDM: 3.796747E−04, and TDM: 3.069266E−04), Sharp ND-R250A5 panel (SDM: 1.124464E−02, DDM: 1.1244639E−02, and TDM: 1.124468E−02) and panel PVM 752 GaAs (SDM: 2.278462E−04, DDM: 1.657951E−04, and TDM: 1.662235E−04) compared to other optimisation algorithms considered in the literature showing improvement in RMSE and competing performance. Furthermore, the Population sizes and iterations based on ablation sensitivity analysis, along with the statistical test-based validation and different fitness function experimental results, proved the superiority of the proposed work over the other existing works in the literature. The research is novel and informative, with the experimental results and statistical analysis providing insight into the technology and business development aspects of PV systems.