A critical review and performance comparisons of swarm-based optimization algorithms in maximum power point tracking of photovoltaic systems under partial shading conditions

This article presents a comparative analysis of the latest swarm-based optimization approaches under partial shading conditions (PSCs) for maximum power point tracking (MPPT) in photovoltaic (PV) systems. The swarm-based MPPT algorithms are stochastic meta-heuristic approaches that have become very popular recently in various applications owing to the drawbacks of conventional MPPT algorithms under different operating conditions. A comprehensive review of the recent research on these algorithms is carried out particularly focusing on the PSCs. The advantages, disadvantages, applications, computational efficiency, and stability of these algorithms are critically surveyed in detail. Moreover, to analyze the comparative performance of the swarm-based algorithms, a special case study is conducted in the MATLAB/Simulink environment for a solar-powered DC load with a boost converter. The performance of seven swarm-based MPPT techniques is evaluated in this case study in terms of their settling time, convergence speed, overshoot, and efficiency under different levels of PSCs. The statistical analysis for 30 simulation runs shows that under heavier shading conditions, the grasshopper optimization algorithm (GOA) and salp swarm algorithm (SSA) outperform other swarm-based MPPT algorithms. It is envisaged that this work will be a one-stop source of guidance for researchers working in the field of MPP optimization under PSCs.

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Published in: Energy Reports
License: http://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.1016/j.egyr.2022.03.175