Experimental validation of an advanced metaheuristic algorithm for maximum power point tracking of a shaded photovoltaic system: A comparative study between three approaches

Abstract

Under partially shaded conditions, the power–voltage characteristic curve of the Photovoltaic System (PVS) presents more than one peak, so the Global Maximum Power Point (GMPP) cannot be detected using the conventional Maximum Power Point Tracking (MPPT) algorithms, such as the Perturb-and-Observe (P&O) algorithm. In order to overcome the limitations of the conventional MPPT algorithms, this paper suggests a metaheuristic MPPT called the Crow Search Algorithm (CSA) for the performance optimization of a standalone PVS. The CSA algorithm has the capability of attenuating the negative effects of the partial shading on the performance of the PVS by the accurate detection of the GMPP. The principle of the latter algorithm uses the crow skills and behaviors in the process of locating places to hide its food. Relative to other metaheuristic methods, the CSA utilizes only two tuning parameters that combine between simplicity of implementation and good efficiency. The simulation and experimental results under partial shading applications demonstrates the better performance of the suggested CSA algorithm compared to the particle swarm optimization and P&O algorithms. In fact, the comparison is carried out in terms of high efficiency, good accuracy, low convergence time and simplicity of implementation. Indeed, the proposed CSA-based MPPT approach extracts the maximum power produced by the PVS with an estimated average efficiency of 99.87%, whereas the PSO and P&O methods record average efficiencies of 99.39% and 95.23%, respectively. Furthermore, as compared to the PSO and P&O MPPT methods, the suggested CSA-based MPPT approach reduces convergence time by an average of 48.41% and 49.63%, respectively.