Volume 7, Issue 2 (February 2020), Pages: 85-90

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 Original research Paper

 Title: Neural network estimation of a photovoltaic system based on the MPPT controller

 Author(s): Marwa Ben Slimene *, Abdulaziz Aljaloud

 Affiliation(s):

 Department of Computer Science and Engineering, College of Computer Science and Engineering, University of Hail, Hail, Saudi Arabia

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-7660-2337

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2020.02.012

 Abstract:

MPPT is necessary to achieve an optimal exploitation of the photovoltaic (PV) system. This paper deals with the problem of the optimization of the power, delivered by the photovoltaic panel (PVP). To achieve this aim, a neural network estimator (NNE), followed by a conversion coefficient and a calculation stage of the optimal duty cycle, has been developed. The NNE is used to calculate the open circuit voltage corresponding to each solar radiation and to a various value of temperature, based only on the standard open circuit voltage. A coefficient, determining for each solar radiation the voltage of the maximum power directly from the open circuit voltage, is estimated by a practical test. Finally, the optimal duty cycle is, next, determined by the input/output equation of the boost converter. The proposed MPPT is tested and compared with the most widely used MPPT methods by simulations using MATLAB/Simulink and real time hardware in the loop (HIL) implementation. The results obtained with the proposed MPPT show excellent dynamic performance under fast irradiation changes. 

 © 2020 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Neural network, Solar radiation sensor, Photovoltaic system, MPPT controller

 Article History: Received 8 September 2019, Received in revised form 16 December 2019, Accepted 19 December 2019

 Acknowledgment:

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest:  The authors declare that they have no conflict of interest.

 Citation:

 Slimene MB and Aljaloud A (2020). Neural network estimation of a photovoltaic system based on the MPPT controller. International Journal of Advanced and Applied Sciences, 7(2): 85-90

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 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 Fig. 9 

 Tables

 Table 1

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