Photovoltaic systemMaximum power point tracking Partial shading conditions Global tracking Artificial intelligence Photovoltaic (PV) energy is a promising source of renewable energy which is sturdy and environmentally friendly. PV generation systems, once installed, produce electricity from solar irradiance without emitting greenhouse gases. To maximize the output power of PV systems, the maximum power point tracking system has been employed (MPPT). The MPPT constitutes a fundamental part of PV systems. In recent years, a large number of MPPT techniques have been proposed. This paper is set up to critically review some of the proposed maximum power point tracking (MPPT) techniques to handle the emergence of multiple MPPs in PV panel characteristics due to the partial shading conditions (PSCs). To define the working principle and the pros and cons of the different proposed techniques clearly and sequentially, they are divided into three groups as follows: conventional MPPT techniques, improved MPPT techniques and artificial intelligence-based MPPT techniques to deal with PSCs. The paper also critically summarizes the findings in terms of their performance in capturing the global maximum power point (GMPP) for PV systems operating under PSCs.
Classic algorithms show high performance in tracking the maximum power point (MPP) of photovoltaic (PV) panels under uniform irradiance and temperature conditions. However, when partial or complex partial shading conditions occur, they fail in capturing the global maximum power point (GMPP) and are trapped in one of the local maximum power points (LMPPs) leading to a loss in power. On the other hand, intelligent algorithms inspired by nature show successful performance in GMPP tracking. In this study, an MPPT system was set up in MATLAB/Simulink software consisting of six groups of serially connected PV panels, a DC-DC boost converter, and load. Using this system, the cuckoo search (CS) algorithm, the modified incremental conductivity (MIC) algorithm, the particle swarm optimization (PSO) algorithm, and the grey wolf optimization (GWO) algorithm were compared in terms of productivity, convergence speed, efficiency, and oscillation under complex shading conditions. The results showed that the GWO algorithm showed superior performance compared to the other algorithms under complex shading conditions. It was observed that GWO did not oscillate during GMPP tracking with an average convergence speed of 0.22 s and a tracking efficiency of 99%. All these evaluations show that GWO is a very fast, highly accurate, efficient, and stable MPPT method under complex partial shading conditions.
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