A performance-guided JAYA algorithm for parameters identification of photovoltaic cell and module

In order to carry out the evaluation, control and maximum power point tracking on photovoltaic (PV) systems, accurate and reliable model parameter identification of PV cell and module is always desired. In this study, a performance-guided JAYA (PGJAYA) algorithm is proposed for extracting parameters of different PV models. In proposed PGJAYA algorithm, the individual performance in the whole population is quantified through probability. Then, based on probability, each individual can self-adaptively select different evolution strategies designed for balancing exploration and exploitation abilities to conduct the searching process. Meanwhile, the quantified performance is employed to select the exemplar to construct the promising searching direction. In addition, a self-adaptive chaotic perturbation mechanism is introduced around the current best solution to explore more better solution for replacing the worst one, thus improving the quality of whole population. The parameters estimation performance of PGJAYA is evaluated through three widely used standard datasets of different PV models including single diode, double diode, and PV module. Comparative and statistical results demonstrate that PGJAYA has a superior performance as it always obtains the most accurate parameters with strong robustness among all compared algorithms. Furthermore, the tests based on experimental data from the data sheet of different types of PV modules suggest that the proposed algorithm can achieve superior results at different irradiance and temperature. Based on these superiorities, it is concluded that PGJAYA is a promising parameter identification method for PV cell and module model.