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Adaptive Maximum Power Point Tracking Using Neural Networks for a Photovoltaic Systems According Grid by H. Sahraoui; H. Mellah; S. Drid; L. Chrifi-Alaoui is a Engineering article available to read on EtoBox.

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Introduction. This article deals with the optimization of the energy conversion of a grid-connected photovoltaic system. The novelty is to develop an intelligent maximum power point tracking technique using artificial neural network algorithms. Purpose. Intelligent maximum power point tracking technique is developed in order to improve the photovoltaic system performances under the variations of the temperature and irradiation. Methods. This work is to calculate and follow the maximum power point for a photovoltaic system operating according to the artificial intelligence mechanism is and the latter is used an adaptive modified perturbation and observation maximum power point tracking algorithm based on function sign to generate an specify duty cycle applied to DC-DC converter, where we use the feed forward artificial neural network type trained by Levenberg-Marquardt backpropagation. Results. The photovoltaic system that we chose to simulate and apply this intelligent technique on it is a stand-alone photovoltaic system. According to the results obtained from simulation of the photovoltaic system using adaptive modified perturbation and observation – artificial neural network the

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Author

H. Sahraoui; H. Mellah; S. Drid; L. Chrifi-Alaoui

Publisher

National Technical University Kharkiv Polytechnic Institute

Published

2021

Language

EN

Field

Engineering (Physical Sciences)