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

• Published in: Energies (Basel) 2019-07, Vol.12 (14), p.2827
• Main Authors: Bouarroudj, Noureddine, Boukhetala, Djamel, Feliu-Batlle, Vicente, Boudjema, Fares, Benlahbib, Boualam, Batoun, Bachir
• Format: Article
• Language: English
• Subjects: Adaptive algorithms; adaptive RBF-NN; Adaptive systems; Algorithms; Alternative energy sources; Back propagation; boost converter; Composition; Computer simulation; Conductance; Control systems design; Controllers; Converters; Current sources; Diodes; Electric potential; Flow charts; Fuzzy logic; Fuzzy sets; Fuzzy systems; Incremental conductance; Irradiance; Load fluctuation; Mathematical models; Maximum power; Methods; MPPT controller; Neural networks; Oscillations; Photovoltaic cells; Photovoltaics; PV-module; Radial basis function; Real numbers; Solar energy; Variables; Voltage; Voltage converters (DC to DC); Weather
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en12142827

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.