Abstract

Maximum power point tracking (MPPT) is a widely used technique to achieve an efficient photovoltaic system in unstable climatic conditions of solar irradiation and temperature. This paper examines the issue of improving the efficiency of a photovoltaic generator (GPV) using an artificial neural network (ANN) based MPPT scheme. Generally, PV modules exhibit nonlinear I −V characteristics with different MPPs depending on the solar irradiation and temperature. To ensure a maximum power transfer to the load form the GPV, it has to operate at its MPP. This is accomplished through
matching impedance between the PV panel and the load using a DC-DC boost converter whose duty cycle is adjusted by artificial neural networks. With respect to the well known perturb and
observe (P&O) MPPT, the obtained simulation results show that the considered ANN based approach is more efficient and oscillations around the MPP are significantly reduced.

Information

Item Type:	Conference
Divisions:	» Laboratory of Electrical Engineering and Renewable Energy » Faculty of Science and Technology
ePrint ID:	2472
Date Deposited:	2020-12-20
Further Information:	Google Scholar
URI:	https://www.univ-soukahras.dz/en/publication/article/2472

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