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Fabrication of Flexible Photovoltaic Cells Read chapter Read first chapter

2021 | OriginalPaper | Chapter

35. Enhancement of Extracted Photovoltaic Power Using Artificial Neural Networks MPPT Controller

Authors : Zerglaine Abdelaziz, Mohammedi Ahmed, Bentata Khadidja, Rekioua Djamila, Oubelaid Adel, Mebarki Nasser Eddine

Published in: Advances in Green Energies and Materials Technology

Publisher: Springer Singapore

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Abstract

Due to the nonlinear electrical characteristics of photovoltaic (PV) generators, the performance and efficiency of such systems can be improved by forcing it to operate at its maximum power point (MPP). In this article, we have proposed an adaptive strategy to achieve maximum power point tracking (MPPT) using an artificial neural network (ANN) approach. ANN control based on neural network learning database is used to control the boost converter fed by an autonomous photovoltaic generator (PVG). The obtained results show that the ANN-MPPT control provides low oscillations and shows good performances around the maximum power point of the PVG compared to the Classical MPPT algorithms such as perturb and observe (P&O).

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Metadata
Title
Enhancement of Extracted Photovoltaic Power Using Artificial Neural Networks MPPT Controller
Authors
Zerglaine Abdelaziz
Mohammedi Ahmed
Bentata Khadidja
Rekioua Djamila
Oubelaid Adel
Mebarki Nasser Eddine
Copyright Year
2021
Publisher
Springer Singapore
Book
Advances in Green Energies and Materials Technology

Print ISBN: 978-981-16-0377-8

Electronic ISBN: 978-981-16-0378-5

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-16-0378-5_35