A New Hybrid Method Based on Gray Wolf Optimizer-Crow Search Algorithm for Maximum Power Point Tracking of Photovoltaic Energy System

In this study, a new hybrid method as gray wolf optimizer (GWO)-crow search algorithm (CSA) (hGWO-CSA) is proposed for solving the MPPT problem in PV energy system. In the proposed method, at first the GWO is applied for MPPT solution and then the optimal duty cycle determined by GWO is considered as the initial value to CSA method. In the hybrid method, the advantages of each method are combined that it is a method with high convergence accuracy and speed and is not trapped in local optimal and quickly achieves to global optimal. The proposed method performance is analyzed in MPPT solution under standard and partial shading condition (PSC), in solar and temperature variations and also considering various types of DC/DC converters. To verify the validity of the hGWO-CSA, the results are compared with GWO and CSA methods. The results show the superiority of the hGWO-CSA in achieving the GMPP with higher convergence speed and less transient oscillations in different condition and in comparison with GWO and CSA methods. Also, the results show that the PV system with the buck-boost converter has better performance due to the wider operation area in terms of extracted power and tracking efficiency than the other DC/DC converters.