Analyzing grid connected shaded photovoltaic systems with steady state stability and crow search MPPT control

Abstract

The field of research in maximum power point tracking (MPPT) methods is making significant progress with a wide range of techniques, from simple yet inefficient approaches to more complex but effective ones. Therefore, it is important to suggest a simple and effective strategy to control the global maximum power point (GMPP) of a photovoltaic (PV) system especially under partial shading conditions (PSC). This paper proposes a novel metaheuristic MPPT called the Crow Search Algorithm (CSA) to ameliorate the tracking performance of a grid connected shaded PV system. The CSA is a nature inspired method based on the intelligent behaviors of crows in its search process for hidden food sources. This novel method succeeds to mitigate the adverse impacts of partial shading on the performance of PV systems by accurately tracking the GMPP. Based on the small-signal dynamic model, the stability of the proposed system is analyzed. Simulation results for three different levels of partial shading, including zero, weak, and severe shading, demonstrate the better performance of the suggested CSA compared to fuzzy logic controller (FLC) and Inc-Cond techniques. In fact, the comparison is carried out in terms of simplicity of implementation, high efficiency, and low power loss, decreasing considerably the convergence time.