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Electronics 2018, 7(1), 4; doi:10.3390/electronics7010004

A Low-Cost Maximum Power Point Tracking System Based on Neural Network Inverse Model Controller

Facultad de Ingeniería, Universidad del Magdalena, Carrera 32 No. 22-08, 470004 Santa Marta, Colombia
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Received: 11 November 2017 / Revised: 27 December 2017 / Accepted: 30 December 2017 / Published: 4 January 2018

Abstract

This work presents the design, modeling, and implementation of a neural network inverse model controller for tracking the maximum power point of a photovoltaic (PV) module. A nonlinear autoregressive network with exogenous inputs (NARX) was implemented in a serial-parallel architecture. The PV module mathematical modeling was developed, a buck converter was designed to operate in the continuous conduction mode with a switching frequency of 20 KHz, and the dynamic neural controller was designed using the Neural Network Toolbox from Matlab/Simulink (MathWorks, Natick, MA, USA), and it was implemented on an open-hardware Arduino Mega board. To obtain the reference signals for the NARX and determine the 65 W PV module behavior, a system made of a 0.8 W PV cell, a temperature sensor, a voltage sensor and a static neural network, was used. To evaluate performance a comparison with the P&O traditional algorithm was done in terms of response time and oscillations around the operating point. Simulation results demonstrated the superiority of neural controller over the P&O. Implementation results showed that approximately the same power is obtained with both controllers, but the P&O controller presents oscillations between 7 W and 10 W, in contrast to the inverse controller, which had oscillations between 1 W and 2 W. View Full-Text
Keywords: neural network inverse model; nonlinear autoregressive network with exogenous inputs; maximum power point tracking MPPT; dc-dc converter; photovoltaic module neural network inverse model; nonlinear autoregressive network with exogenous inputs; maximum power point tracking MPPT; dc-dc converter; photovoltaic module
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Robles Algarín, C.; Sevilla Hernández, D.; Restrepo Leal, D. A Low-Cost Maximum Power Point Tracking System Based on Neural Network Inverse Model Controller. Electronics 2018, 7, 4.

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