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ِِِAhmed G. Abdullah
Northern Technical University
Computer Science & IT Electrical & Electronics Engineering

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Comparison between neural network and P&O method in optimizing MPPT control for photovoltaic cell ِِِAhmed G. Abdullah; Mothanna Sh. Aziz; Bashar Abdullah Hamad
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (798.633 KB) | DOI: 10.11591/ijece.v10i5.pp5083-5092

Abstract

The demand for renewable energy has increased because it is considered a clean energy and does not result in any pollution or emission of toxic gases that negatively affect the environment and human health also requiring little maintenance, and emitting no noise, so it is necessary to develop this type of energy and increase its production capacity. In this research a design of maximum power point tracking (MPPT) control method using Neural Network (NN) for photovoltaic system is presented. First we design a standalone PV system linked to dc boost chopper with MPPT by perturbation and observation P&O technique, and then a design of MPPT by using ANN for the same system is presented. Comparative between two control methods are studied. The results explained in constant and adjustable weather settings such as irradiation and temperature. The results exposed that the proposed MPPT by ANN control can improve the PV array efficiency by reduce the oscillation around the MPP that accure in P&O method and so decreases the power losses. As well as decrease the the overshot that accure in transient response, and hence improving the performance of the solar cell.
Co-Authors Bashar Abdullah Hamad Mothanna Sh. Aziz