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This chapter presents a comprehensive study of a new hybrid method developed for obtaining the electrical unknown parameters of solar cells. The combination of a traditional method and a recent smart swarm-based optimization method is done, with a big focus on the application of the topic of artificial intelligence algorithms into solar photovoltaic production. The combined approach was done between the traditional method, which is the noniterative Levenberg-Marquardt technic and between the recent meta-heuristic optimization technic, called Grey Wolf optimizer algorithm. For comparison purposes, some other classical solar cell parameter determination optimization-based methods are carried out, such as the numerical (iterative, noniterative) methods, the meta-heuristics (evolution, human, physic, and swarm) methods, and other hybrid methods. The final obtained results show that the used hybrid method outperforms the above-mentioned classical methods, under this study.

This paper presents a summary and comparative study of methods used for getting electrical unknown parameters of photovoltaic cells/panels. The exact parameters values are essential for precise mathematical modeling, simulation, and control of the photovoltaic generation systems. The many different methods are presented, discussed, and classified (general, analytical, numerical, optimization, adaptive). For comparison purposes, for each category of classification, a comparison among the best ones of them is done. An evaluation is elaborated based on the several chosen objectives function existing for the optimization-based approach. Besides, the performance of each used method is analyzed in relations of some norms: accuracy, ease of implementation, speed of convergence, computational complexity and quality of the obtained results. An effective evaluation under various climatic conditions, for diverse technologies materials, and for different manufacturers was discussed. Some statistical analysis is used to choose the best precise quality of the fitting curves to the real data. Some critical analysis is done with some evaluations, which serve to elaborate on this survey and synthesis study, which will be useful for researchers in the photovoltaic topic.