<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Photovoltaic (PVS) generators’ nonlinear electrical characteristics allow for greater performance and efficiency when they are forced to operate at their peak power (MPP). This article suggests an adaptive method for maximizing power point tracking that makes use of artificial neural network (ANN) techniques (MPPT). A step-up converter powered by a separate solar generator is under the control of an ANN controller built on a neural network training database (PVS). The results show that ANN-MPPT has good control performance and is near to the maximum power point of PVS when compared to conventional MPPT methods like perturb and observe and incremental conductance.</p>
</abstract>

Photovoltaic (PVS) generators’ nonlinear electrical characteristics allow for greater performance and efficiency when they are forced to operate at their peak power (MPP). This article suggests an adaptive method for maximizing power point tracking that makes use of artificial neural network (ANN) techniques (MPPT). A step-up converter powered by a separate solar generator is under the control of an ANN controller built on a neural network training database (PVS). The results show that ANN-MPPT has good control performance and is near to the maximum power point of PVS when compared to conventional MPPT methods like perturb and observe and incremental conductance.

Improved Artificial Neural Network Design for MPPT Grid-Connected Photovoltaic Systems

The Scientific Bulletin of Electrical Engineering Faculty

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Photovoltaic (PVS) generators’ nonlinear electrical characteristics allow for greater performance and efficiency when they are forced to operate at their peak...

Improved Artificial Neural Network Design for MPPT Grid-Connected Photovoltaic Systems

Pubblicato online: 15 apr 2023

Pagine: 26 - 31

DOI: https://doi.org/10.2478/sbeef-2022-0016

Parole chiave
Photovoltaic system, Boost converter, P&O and INC control, ANN Controller

© 2023 Saliha Maarouf et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Improved Artificial Neural Network Design for MPPT Grid-Connected Photovoltaic Systems

Pubblicato online: 15 apr 2023

Pagine: 26 - 31

DOI: https://doi.org/10.2478/sbeef-2022-0016

Parole chiavePhotovoltaic system, Boost converter, P&O and INC control, ANN Controller

© 2023 Saliha Maarouf et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
Photovoltaic system, Boost converter, P&O and INC control, ANN Controller