A new hybrid algorithm based on grey wolf optimizer and cuckoo search for parameter extraction of solar photovoltaic models

Quickly, accurately and reliably extract the parameters of solar photovoltaic (PV) model is very critical to simulate, evaluate and control the PV systems. During the past few years, many analytical, numerical and meta-heuristic algorithms have been suggested to extract the parameters of PV models based on the experimental data. However, extracting the parameters of PV models is still a great challenge. In this paper, a new hybrid algorithm based on grey wolf optimizer and cuckoo search (GWOCS) is developed to extract the parameters of different PV cell models with the experimental data under different operating conditions. In GWOCS, a new opposition learning strategy for the decision layer individuals (i.e., α, β, and δ) is proposed to enhance diversity of GWO. The main advantage of GWOCS is its ability to balance between exploration and exploitation. The performance of GWOCS is firstly tested on 10 complex benchmark functions. Then, the GWOCS is applied to extract the parameters of several solar PV cell models under different operating conditions. The comprehensively experimental results show the GWOCS is a promising candidate approach to extract the parameters of solar PV models.