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Comparative Analysis of the Performance of Maximum Power Point Tracking Algorithms in Photovoltaic Systems

Abstract:

Photovoltaic energy production has shown exponential growth in recent years due to the reduction of costs in the manufacture of solar panels and the awareness of generating new alternatives for clean energy production that produce a near-zero impact on the environment. The purpose of this research is to compare the performance of five maximum power point tracking (MPPT) algorithms for a photovoltaic system to determine the maximum energy extraction from the solar panels considering varying weather conditions. In addition, the modeling of a solar panel is carried out based on the equivalent electronic circuit model analysis. The conventional MPPT algorithms, such as Perturbation and Observation (P&O) and Incremental Conductance (INC), and three more advanced and robust algorithms based on Fuzzy Logic Control (FLC), Sliding Mode Control (SMC), and Neural Networks (NN) are developed. The designed schemes of a solar panel, the converter, and controllers are tested using Matlab-Simulink in different simulation scenarios. Results show that SMC and NN algorithms present better behavior related to P&O and INC algorithms than the other studied algorithms.