Performance Comparison of Grey Wolf and Perturb&Observe MPPT Algorithms in Different Weather Conditions

This study compares the performances of Meta-heuristic grey wolf optimization (GWO) and analytical-based Perturb and Observe (P&O) algorithms to extract maximum power from a photovoltaic system exposed to rapid variation of sunlight and partial shading conditions. Theoretical models were created using the Pvlib library, a new and flexible module in the Python environment. Algorithm performances have been evaluated on the DC-DC step-down converter built using the PySpice/Python library. The results show that the GWO algorithm provides superior tracking performance in different weather conditions than P&O.