Evaluation of particle swarm optimization techniques applied to maximum power point tracking in photovoltaic systems

Abstract

SummaryEven with significant progresses in the maximum power point (MPP) research area, the necessity to improve the existing methods becomes mandatory to increase the energy conversion efficiency. Since the power–voltage (P‐V) characteristic curve of photovoltaic (PV) arrays has multiple peaks under partially shaded (PS) conditions, the conventional MPP tracking (MPPT) control methods have the difficult challenge of locate the global MPP (GMPP) among many local MPPs (LMPPs). In recent years, numerous research papers have been focused on techniques to efficiently track the GMPP and alleviate the partial shading effects. One of the most popular evolutionary search technique is particle swarm optimization (PSO) that provides high tracking speed and the ability operate under different environmental conditions. For solving some conventional PSO technique common weaknesses, several modifications and improvements have emerged in the past years. This paper provides a comparative and comprehensive review of some relevant PSO‐based methods taking into account the effects of important key issues such as particles initialization criteria, search space, convergence speed, initial parameters, performance with and without partial shading, and efficiency. The simulation results are validated under numerous test conditions using MATLAB code and Simulink package.

Improving dynamic efficiency of photovoltaic generation systems using adaptive type 2 fuzzy‐neural network via EN 50530 test procedure

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Parameter extraction of single‐diode photovoltaic module using experimental current–voltage data

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A Comparison between Particle Swarm and Grey Wolf Optimization Algorithms for Improving the Battery Autonomy in a Photovoltaic System

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