Optimizing Photovoltaic System Performance through the Design and Development of an Artificial Neural Network MPPT Control-Reference-Cited by-同舟云学术

Optimizing Photovoltaic System Performance through the Design and Development of an Artificial Neural Network MPPT Control

Published:2024-03-21 Issue: Volume: Page:
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Author:

Meddah Mohamed¹,Belarbi Ahmed Wahid¹,Negadi Karim²,Djaballah Younes³

Affiliation:

1. Laboratory of Physics and Electrical Discharge, Electrotechnical Engineering Laboratory, University of Sciences and Technology of Oran

2. L2GEGI Laboratory, Department of Electrical Engineering, Faculty of Applied Science, University of Tiaret, BP 78 Zaaroura, 14000

3. Applied automation and diagnostic industrial laboratory (LAADI), Ziane Achour University of Djelfa, BP 3117

Abstract

Abstract This article conducts a thorough comparison of three Maximum Power Point Tracking control techniques for photovoltaic systems: Perturb and Observe, Incremental Conductance, and Artificial Neural Network. The study aims to identify the most effective MPPT method by subjecting each technique to numerical simulations. The article explores the performance, efficiency, and robustness of Perturb and Observe, Incremental Conductance and Artificial Neural Network in capturing the maximum power output from photovoltaic panels under varying environmental conditions. Following rigorous testing through numerical simulations, the superior technique is selected for implementation in a grid-connected photovoltaic power conversion chain. This research contributes valuable insights into the optimization of photovoltaic system performance through advanced MPPT control strategies, facilitating informed decisions for practical applications in renewable energy systems.

Publisher

Research Square Platform LLC

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