A Low-Cost Maximum Power Point Tracking System Based on Neural Network Inverse Model Controller
AbstractThis 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
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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.
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(1):4.Chicago/Turabian Style
Robles Algarín, Carlos; Sevilla Hernández, Deimer; Restrepo Leal, Diego. 2018. "A Low-Cost Maximum Power Point Tracking System Based on Neural Network Inverse Model Controller." Electronics 7, no. 1: 4.
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