Skip to main navigation menu Skip to main content Skip to site footer
×
Español (España) | English
Editorial
Home
Indexing
Original

Improving Photovoltaic System Performance with Artificial Neural Network Control

By
BENCHIKH Salma ,
BENCHIKH Salma

National school of applied sciences, Ibn Tofail University, Advanced systems engineering laboratory. Kenitra, Morocco.

Search this author on:

PubMed | Google Scholar
JAROU Tarik ,
JAROU Tarik

National school of applied sciences, Ibn Tofail University, Advanced systems engineering laboratory. Kenitra, Morocco.

Search this author on:

PubMed | Google Scholar
BOUTAHIR Mohamed Khalifa ,
BOUTAHIR Mohamed Khalifa

Faculty of Sciences and Techniques, Moulay Ismail University, Engineering Science and Technology Laboratory. Errachidia, Morocco.

Search this author on:

PubMed | Google Scholar
NASRI Elmehdi ,
NASRI Elmehdi

National school of applied sciences, Ibn Tofail University, Advanced systems engineering laboratory. Kenitra, Morocco.

Search this author on:

PubMed | Google Scholar
LAMRANI Roa ,
LAMRANI Roa

National school of applied sciences, Ibn Tofail University, Advanced systems engineering laboratory. Kenitra, Morocco

Search this author on:

PubMed | Google Scholar

Abstract

Photovoltaic systems play a pivotal role in renewable energy initiatives. To enhance the efficiency of solar panels amid changing environmental conditions, effective Maximum Power Point Tracking (MPPT) is essential. This study introduces an innovative control approach based on an Artificial Neural Network (ANN) controller tailored for photovoltaic systems. The aim is to elevate the precision and adaptability of MPPT, thereby improving solar energy harvesting. This research integrated an ANN controller into a photovoltaic system in order dynamically optimize the operating point of solar panels in response to environmental changes. The performance of the ANN controller was compared with traditional MPPT approaches using simulation in Simulink/Matlab. The results of the simulation showed that the ANN controller performed better than the traditional MPPT techniques, highlighting the effectiveness of this method for dynamically changing solar panel performance. The ANN particularly demonstrates higher precision and adaptability when environmental conditions vary. The strategy consistently achieves and maintains the maximum power point, enhancing overall energy harvesting efficiency. The integration of an ANN controller marks a significant advance in solar energy control. The study highlights the superiority of the ANN controller through rigorous simulations, demonstrating increased accuracy and adaptability. This approach not only proves effective, but also has the potential to outperform other MPPT strategies in terms of stability and responsiveness.

How to Cite

1.
BENCHIKH S, JAROU T, BOUTAHIR MK, NASRI E, ELAMRANI R. Improving Photovoltaic System Performance with Artificial Neural Network Control . Data and Metadata [Internet]. 2023 Dec. 30 [cited 2024 Apr. 24];2:144. Available from: https://dm.saludcyt.ar/index.php/dm/article/view/144

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

Article metrics

Google scholar: See link

Metrics

Metrics Loading ...

The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.