Analysis of maximum-power-tracking methods in photovoltaic panels

Abstract

In the past century, the demand for energy has soared with the rapid development of technology and the human population. Fossil fuels such as coal, natural gas and oil used today to obtain energy have a limited life span. Therefore, the interest in renewable energy sources is increasing. Solar energy, which is one of these renewable energy sources, comes to the fore more than the other renewable energy sources because the energy emitted instantly by the sun is so much greater than the energy consumed by the world in a year. Therefore, much research has been done to produce a greater amount of energy from the sun. This study examines the maximum-power-point tracking methods that are widely used today. The methods under review include constant voltage, short-circuit current, perturb and observe, fuzzy logic, incremental conductance and artificial neural networks. Of these methods, perturb and observe, fuzzy logic and artificial neural networks have been analyzed using the PVSOL software, which is for making designs and simulations for photovoltaic systems. The analysis includes a comparison of the depreciation periods for standard systems and facilities where these control algorithms are used.