Parameter estimation of different solar cells using a novel swarm intelligence technique

Since the demand for a clean source of energy has increased, it led to the consequent rise in the importance of solar energy. Thus, the concept of solar cell modelling has drawn the attention of various researchers across the world. A useful and accurate mathematical model for such cells is therefore necessary. In the literature, the three-diode model is suggested as a more reliable approach to satisfy the behaviour of photovoltaic (PV) cells. This paper proposes a new swarm intelligent technique, namely the chaotic chicken swarm optimization, which is originated from the parent chicken swarm algorithm, for parameter assessment of the three-diode PV model, as the three-diode PV model incorporate the grain boundaries and leakage current. Ten different popular chaotic maps have been considered for the study to identify the parameters from the manufacturer datasheet. The chicken swarm technique yields great optimization results both in terms of accuracy and robustness. Further, the use of chaotic maps improves the diversification feature of this metaheuristic technique for which it is preferred. The performance of the proposed approach proved better when compared with some of the popular techniques available in the literature in terms of convergence accuracy and speed. The accuracy of the proposed technique is also verified by extracting I–V and P–V curves. Moreover, several nonparametric statistical tests are also performed to validate the significance of the outcomes obtained by the proposed method.