International Journal of Environmental Science and Development

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).

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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.