Maximum Power Point Tracker Based on Fuzzy Adaptive Radial Basis Function Neural Network for PV-System

Noureddine Bouarroudj, Djamel Boukhetala, Vicente Feliu-Batlle, Fares Boudjema, Boualam Benlahbib and Bachir Batoun
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Noureddine Bouarroudj: Unité de Recherche Appliquée en Energies Renouvelables, URAER, Centre de Développement des Energies Renouvelables, CDER, Ghardaïa 47133, Algeria
Djamel Boukhetala: Laboratoire de Commande des Processus, Ecole Nationale Polytechnique, 10 Rue des Frères OUDEK, El-Harrach, Alger 16200, Algeria
Vicente Feliu-Batlle: School of Industrial Engineering, University of Castilla-La Mancha, Av. Camilo Jose Cela, S/N, C.P. 13001 Ciudad Real, Spain
Fares Boudjema: Laboratoire de Commande des Processus, Ecole Nationale Polytechnique, 10 Rue des Frères OUDEK, El-Harrach, Alger 16200, Algeria
Boualam Benlahbib: Unité de Recherche Appliquée en Energies Renouvelables, URAER, Centre de Développement des Energies Renouvelables, CDER, Ghardaïa 47133, Algeria
Bachir Batoun: Unité de Recherche Appliquée en Energies Renouvelables, URAER, Centre de Développement des Energies Renouvelables, CDER, Ghardaïa 47133, Algeria

Energies, 2019, vol. 12, issue 14, 1-19

Abstract: In this article, a novel maximum power point tracking (MPPT) controller for a photovoltaic (PV) system is presented. The proposed MPPT controller was designed in order to extract the maximum of power from the PV-module and reduce the oscillations once the maximum power point (MPP) had been achieved. To reach this goal, a combination of fuzzy logic and an adaptive radial basis function neural network (RBF-NN) was used to drive a DC-DC Boost converter which was used to link the PV-module and a resistive load. First, a fuzzy logic system, whose single input was based on the incremental conductance (INC) method, was used for a variable voltage step size searching while reducing the oscillations around the MPP. Second, an RBF-NN controller was developed to keep the PV-module voltage at the optimal voltage generated from the first stage. To ensure a real MPPT in all cases (change of weather conditions and load variation) an adaptive law based on backpropagation algorithm with the gradient descent method was used to tune the weights of RBF-NN in order to minimize a mean-squared-error (MSE) criterion. Finally, through the simulation results, our proposed MPPT method outperforms the classical P and O and INC-adaptive RBF-NN in terms of efficiency.

Keywords: PV-module; boost converter; MPPT controller; fuzzy logic; adaptive RBF-NN (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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