Hybrid maximum power point tracking technique based on GWO-P&O for photovoltaic system under various conditions

The partial shading condition is the main weak point that affects the output power of photovoltaic systems, due to imprecise tracking of the global maximum power point. In partial shading conditions with many maximum power points on the Power–Voltage (P-V) curve, traditional algorithms such as perturb & observe, incremental conductance algorithms are failing to track the global maximum power point, thus reducing the overall PV system efficiency. This study proposes a hybrid GWO-P&O algorithm that combines the grey wolf optimization algorithm with the perturb & observe algorithm for extracting the maximum power from a PV system exposed to rapid variations of solar irradiation and partial shading conditions. The grey wolf optimization algorithm handles the initial phases followed by Perturb & Observe algorithm at the final phase to achieve faster convergence to the global peak. The idea behind using the hybrid technique is to scale down the search space of GWO which helps to speed up to achieve convergence towards the global peak. The hybrid GWO-P&O algorithm is first implemented using MATLAB/SIMULINK, then an experimental setup is designed for its practical implementation. The results of the proposed algorithm are compared with the traditional P&O algorithm, the traditional GWO algorithm, and the PSO algorithm. From the results, it has been proved that the proposed algorithm has high tracking efficiency, low oscillations around MPP, fast convergence toward MPP, high accuracy, and small-time to reach MPP compared to other algorithms.