Comparative Analysis of ABC, Bat, GWO and PSO Algorithms for MPPT in PV Systems

• Published in: International Conference on Renewable Energy Research and Applications (Online) pp. 347 - 352
• Main Authors: da Rocha, Maykon Vichoski, Sampaio, Leonardo Poltronieri, Oliveira da Silva, Sergio Augusto
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2019
• Subjects: Ant Bee Colony; Bat; Convergence; Flowcharts; Grey Wolf Optimization; Maximum Power Point; Maximum power point trackers; Oscillators; Partial Shading Conditions; Particle Swarm Optimization; Radiation effects; Renewable energy sources; Simulation
• ISSN: 2572-6013
• DOI: 10.1109/ICRERA47325.2019.8996520

Traditional algorithms used to perform the maximum power point tracking (MPPT) may not reach the global maximum power point (GMPP) when the photovoltaic (PV) modules are subjected to partial shading condition. Therefore, this paper presents a comparative analysis involving four MPPT algorithms employed in a PV system subject to partial shading condition, being these four MPPT techniques based on artificial bee colony, bat, grey wolf optimization and particle swarm optimization algorithms, respectively. These algorithms are always able to track the GMPP, presenting lower power oscillations in steady-state. In addition, these algorithms are evaluated and compared to each other taking into account three different cases: (i) PV array operating at standard test condition with uniform solar irradiation; (ii) and (iii) PV array under different cases of partial shading. By means of computational simulation results, the performances of the presented MPPT algorithm are evaluated and compared to each other considering the power oscillations in steady-state, the tracking factor, as well as the convergence time to reach the GMPP.