| Issue |
E3S Web of Conf.
Volume 469, 2023
The International Conference on Energy and Green Computing (ICEGC’2023)
|
|
|---|---|---|
| Article Number | 00003 | |
| Number of page(s) | 15 | |
| DOI | https://doi.org/10.1051/e3sconf/202346900003 | |
| Published online | 20 December 2023 | |
Comparative Analysis between Proportional-Integral and Artificial Neural Network Control of a Grid-Connected PV System
1 Materials and Instrumentation (MIM), High School of Technology, Moulay Ismail University, Meknes, Morocco.
2 Industrial Systems Engineering and Energy Conversion Team, FSTT, Abdelmalek Essaadi University, Tetouan, Morocco.
* Corresponding author: radouan.gouaamar@gmail.com
This article presents a comprehensive analysis of the modeling and control techniques applied to a photovoltaic (PV) system that is connected to a three-phase grid. To successfully integrate the PV system with the electrical grid, an innovative and reliable controller has been designed and put into practice. The utilization of an artificial neural network (ANN) enables the system to optimize power extraction from the PV panels, benefiting from the ANN's resilience and swift response to varying conditions. Moreover, a robust proportional-integral (PI) control strategy is introduced to govern the grid-side operations. This strategy of action focuses on managing the injection of both active and reactive electricity into the grid as well as controlling the voltage of the DC bus. A series of detailed simulations were carried out evaluating the efficiency of the suggested control strategy in the MATLAB/SIMULINK. The results obtained from these simulations share insightful information on the effectiveness and efficiency of the control system in ensuring optimal operation and power management of the PV system within the grid-connected setup.
Key words: photovoltaic (PV) / artificial neural network / maximum power point tracking (MPPT) / proportional-integral (PI) / Perturb & Observe(P&O)
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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