DMPPT control of photovoltaic systems under partial shading conditions based on optimized neural networks

Abstract

When solar irradiation is uniform along with the array, the P–V curve represents a unique maximum power point (MPP). If the cells undergo shade conditions in the presence of bypass diodes, the solar array’s power is decreased, and the P–V curve of the array represents multiple local MPPs (LMPP) and a global MPP (GMPP). LMPPs might mislead the maximum power point tracking (MPPT) algorithms because their characteristics are identical to the MPP. Various studies have been conducted on partial shading conditions. This study uses parallel distributed maximum power point tracking (DMPPT) due to the advantages of this structure. A high-gain converter is presented to resolve the high conversion gain required by the DC/DC converter in this structure. This study also presents MLP and RBF networks for MPP tracking and compares their efficiencies under the same irradiation and partial shading conditions. Since determining optimal weight coefficients in MLP neural networks and variances, means, and weights in RBF networks play an essential role in their performance, this study uses four optimization algorithms of particle swarm optimization (PSO), gray wolf optimization (GWO), grasshopper optimization algorithm (GOA), and Harris Hawks optimization (HHO). Finally, an adaptive fuzzy-PID controller controls the three-phase grid-connected inverter. A comparison of the results shows that the efficiency of MLP and RBFII is almost the same, about 98–99%. Moreover, the accuracy of MLP networks is higher than RBF, and RBF networks’ only advantage is shorter training time. In addition, RBF networks require much more activation functions for proper performance. The simulation outcomes confirm the superior efficacy of the HHO algorithm in training neural networks when compared to alternative algorithms.