Light & Engineering 29 (1)

Volume 29
Date of publication 02/27/2021
Pages 69–76

New Photovoltaic Module Model And A Comparative Study of MPPT Control Techniques Based On Neural Networks Light & Engineering Vol. 29, No. 1

Articles authors:

Mourad Talbi, Nawel Mensia, Hatem Ezzaouia

Mourad Talbi, Ph. D., an Associate Professor in Electrical Engineering at the Centre of Researches and Technologies of Energy of Borj Cedria (CRTEn), Tunis, Tunisia. He is a member of Laboratory of Nano-Materials and Systems for Renewable Energies (LaNSER). He has obtained his Master degree in automatics and signal processing at National Engineering School of Tunis in 2004. He has obtained his Ph. D. in Electronics at Faculty of Sciences of Tunis in 2010, and his HDR in Electronics at Faculty of sciences of Tunis in 2015

Nawel Mensia, Ph. D., an Assistant Professor in Electrical Engineering at the Centre of Researches and Technologies of Energy of Borj Cedria (CRTEn), Tunis, Tunisia. She is a member of a Photovoltaic Laboratory (CRTEn). She received her Ph. D. in Electrical Engineering from Tunis-Elmanar University, in 2011. Her research interests include application of advanced control in photovoltaic systems

Hatem Ezzaouia, Ph.D. Since 2015 he is Lab Director of LSNTA (Semiconductor Laboratory, Nanostructures and Advanced Technology at CRTEn)

Abstract:

Nowadays, renewable energy resources play an important role in replacing conventional fossil fuel energy resources. Solar photovoltaic (PV) energy is a very promising renewable energy resource, which rapidly grew in the past few years. The main problem of the solar photovoltaic is with the variation of the operating conditions of the array, the voltage at which maximum power can be obtained from it likewise changes. In this paper, is first performed the modelling of a solar PV panel using MATLAB/Simulink. After that, a maximum power point tracking (MPPT) technique based on artificial neural network (ANN) is applied in order to control the DC-DC boost converter. This MPPT controller technique is evaluated and compared to the “perturb and observe” technique (P&O). The simulation results show that the proposed MPPT technique based on ANN gives faster response than the conventional P&O technique, under rapid variations of operating conditions. This comparative study is made in terms of temporal variations of the duty cycle (D), the output power ( out P ), the output current ( out I ), the efficiency, and the reference current ( ref I ). The efficiency, D, out P , and out I are the output of the boost DC-DC, and ref I is itsinput. The different temporal variations of the efficiency, D, ref I , out P , and out I (for the two cases: the first case, when T = 25°C and G =1000 W/m2 and the second case, when T and G are variables), show negligible oscillations around the maximum power point. The used MPPT controller based on ANN has a convergence time better than conventional P&O technique.

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Keywords

DOI: https://doi.org/10.33383/2019-057

Article in RUS:
Svetotekhnika # 6, 2020, pp. 46–52

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