A Robust Single-Sensor MPPT Strategy for Shaded Photovoltaic-Battery System

A robust single-sensor global maximum power point tracking (MPPT) strategy based on modern optimization for photovoltaic systems considering shading conditions is proposed in this work. The proposed strategy is designed for battery charging applications and direct current (DC) microgrids. Under normal operation, the curve of photovoltaic (PV) output power versus PV voltage contains only a single peak point. This point can be simply captured using any traditional tracking method like perturb and observe. However, this situation is completely different during the shadowing effect where several peaks appear on the power voltage curve. Most of these peaks are local with only a single global. This condition leads to the incapability of traditional tracking approaches to extract the global peak power due to their inability to distinguish between the local and global peak points. They are trapped in the first peak point even when the point is local. Therefore, global tracking approaches based on modern optimization are highly required. A recent marine predators algorithm (MPA) has been used to solve the problem of tracking the global MPP under shadowing influence. Different shadowing scenarios are used to test and evaluate the performance of MPA based tracker. The obtained results are compared with particle swarm optimization (PSO) and ant lion optimizer (ALO). The results of the comparison confirmed the effectiveness and robustness of the proposed global MPPT-MPA based tracker over PSO and ALO.