Novel parameter extraction for Single, Double, and three diodes photovoltaic models based on robust adaptive arithmetic optimization algorithm and adaptive damping method of Berndt-Hall-Hall-Hausman

Experimental data-oriented parameter extraction helps in giving an accurate assessment to forecast the output current of the photovoltaic cells. This may be very beneficial in the modeling, optimization, partial shading, and assessment of the PV systems both practically and conceptually. Attributed to the reason that hybrid techniques incorporate the advantages of two or more algorithms, they outperform stochastic methods in solving the given complex multi-dimensional and multi-model optimization problems. Many authors have proposed hybrid methods to improve precision, steadiness, and overall efficiency. However, most of the algorithms concentrate on the methodology in the literature, and little attention has been paid to the objective function formulation, leading to a theoretical void in this area of research. In this work, we present a robust adaptive Arithmetic Optimization Algorithm based on the adaptive damping Berndt-hall-hall-Hausman (RaAOAAdBHHH) approach to efficacity determine the parameters of the single, double, and three diode PV model. The experimental results demonstrate that the proposed RaAOAAdBHHH approach successfully minimizes error to zero with rapid convergence, as determined by different statistical criteria and compared to experimental data.

Automated summary

Experimental data-oriented parameter extraction is crucial for accurately assessing the output current of photovoltaic cells. In this study, a robust adaptive arithmetic optimization algorithm based on the adaptive damping Berndt-hall-hall-Hausman approach is proposed to determine the parameters of single, double, and three diode PV models. The experimental results demonstrate the effectiveness and rapid convergence of the proposed approach.