IJESD 2016 Vol.7(9): 642-645 ISSN: 2010-0264
doi: 10.18178/ijesd.2016.7.9.854
doi: 10.18178/ijesd.2016.7.9.854
Optimized Photovoltaic Power Generator Using Artificial Neural Network Implementation for Maximum Power Point Tracking
N. Ghedhab and F. Youcefettoumi
Abstract—A photovoltaic generator present nonlinear
voltage-current characteristics. A boost converter is used to
match the photovoltaic system to the load charge and to operate
the photovoltaic cell array at maximum power point. This paper
presents an application of a neural network to identify the
optimal operating point of the photovoltaic module. The power
output from the modules depends on the environmental factors
such as cell temperature and solar irradiation. Therefore,
accurate identification of optimal operating point and
continuous control of boost converter are required to achieve the
maximum output efficiency. The proposed neural network has a
simple structure and provides an accurate identification of the
optimal operating point and also an accurate estimation of the
maximum power from the photovoltaic modules. The proposed
model is compared with conventional Perturb and Observe
technique and shown that Artificial Neural Network can
increase the overall system efficiency by approximately 10%.
Index Terms—Artificial neural network (ANN), maximum power point tracking (MPPT), perturb and observe algorithm (P&O), photovoltaic (PV) module.
The authors are with the Department of Electronics Engineering in University of Science and Technology Houari Boumedien, Algeria (e-mail: ghedhabnabila1@hotmail.fr, youcefettoumi@yahoo.fr).
Index Terms—Artificial neural network (ANN), maximum power point tracking (MPPT), perturb and observe algorithm (P&O), photovoltaic (PV) module.
The authors are with the Department of Electronics Engineering in University of Science and Technology Houari Boumedien, Algeria (e-mail: ghedhabnabila1@hotmail.fr, youcefettoumi@yahoo.fr).
Cite: N. Ghedhab and F. Youcefettoumi, "Optimized Photovoltaic Power Generator Using Artificial Neural Network Implementation for Maximum Power Point Tracking," International Journal of Environmental Science and Development vol. 7, no. 9, pp. 642-645, 2016.
General Information
- ISSN: 2010-0264 (Print); 2972-3698 (Online)
- Abbreviated Title: Int. J. Environ. Sci. Dev.
- Frequency: Bimonthly
- DOI: 10.18178/IJESD
- Editor-in-Chief: Prof. Richard Haynes
- Managing Editor: Ms. Cherry L. Chen
- Indexing: Scopus (CiteScore 2022: 1.4), Google Scholar, CNKI, ProQuest, EBSCO, etc.
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