Suitable mathematical model for the electrical characterization of different photovoltaic technologies: Experimental validation

Different photovoltaic technologies have achieved notable improvements in efficiency in recent years, allowing photovoltaic cells to convert a higher percentage of incident solar radiation into electrical energy. However, the search for new, more efficient technologies as well as the improvement of existing ones, remains the aim of researchers and manufacturers. New materials have been considered, requiring the development of more accurate mathematical models to simulate their behavior. This study focusses on the validation of a modified multidimension diode model to determine a suitable model for different photovoltaic technologies. This model includes m-diodes connected in series and u-strings of diodes in parallel, providing a reconfigurable diode network, which allows the determination of the optimal number of diodes in the equivalent electrical circuit based on manufacturer or experimental data. The guaranteed convergence particle swarm optimization algorithm was used to estimate the photovoltaic parameters of the modified multidimension diode model. The validation was carried out with six different photovoltaic technologies, namely polycrystalline silicon, mono crystalline silicon, amorphous silicon, copper indium selenide, heterojunction with intrinsic thin-layer, and cadmium sulfide/cadmium telluride. The results revealed that the modified multidimension diode model accurately reflects the behavior of different photovoltaic technologies with high performance, providing a good compromise between accuracy and computational cost. Specifically, the modified multidimension diode model, when compared to the single-diode model (most commonly used in literature), achieved up to 14% more accuracy for the photovoltaic technologies considered under this study. Furthermore, the mathematical models identified as most suitable for the photovoltaic technologies under study are suggested as references for future works.