Version 1
: Received: 10 June 2024 / Approved: 11 June 2024 / Online: 12 June 2024 (08:15:18 CEST)
How to cite:
Sørensen, R.; Mihet-Popa, L. Development and Comparative Evaluation of Traditional and Advanced MPPT Algorithms for PV Systems in Partial Shading Conditions. Preprints2024, 2024060788. https://doi.org/10.20944/preprints202406.0788.v1
Sørensen, R.; Mihet-Popa, L. Development and Comparative Evaluation of Traditional and Advanced MPPT Algorithms for PV Systems in Partial Shading Conditions. Preprints 2024, 2024060788. https://doi.org/10.20944/preprints202406.0788.v1
Sørensen, R.; Mihet-Popa, L. Development and Comparative Evaluation of Traditional and Advanced MPPT Algorithms for PV Systems in Partial Shading Conditions. Preprints2024, 2024060788. https://doi.org/10.20944/preprints202406.0788.v1
APA Style
Sørensen, R., & Mihet-Popa, L. (2024). Development and Comparative Evaluation of Traditional and Advanced MPPT Algorithms for PV Systems in Partial Shading Conditions. Preprints. https://doi.org/10.20944/preprints202406.0788.v1
Chicago/Turabian Style
Sørensen, R. and Lucian Mihet-Popa. 2024 "Development and Comparative Evaluation of Traditional and Advanced MPPT Algorithms for PV Systems in Partial Shading Conditions" Preprints. https://doi.org/10.20944/preprints202406.0788.v1
Abstract
The optimization of photovoltaic (PV) systems is vital for enhancing efficiency and economic viability, especially under Partial Shading Conditions (PSCs). This study focuses on the development and comparison of traditional and advanced algorithms, including Perturb and Observe (P&O), Incremental Conductance (IC), Fuzzy Logic Control (FLC), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Artificial Neural Networks (ANN), for efficient Maximum Power Point Tracking (MPPT). Simulations conducted in MATLAB/SIMULINK evaluated these algorithms’ performance under various shading scenarios. The results indicate that while traditional methods like P&O and IC are effective under uniform conditions, advanced techniques, particularly ANN-based MPPT, exhibit superior efficiency and faster convergence under PSCs. The study concludes that integrating AI and ML into MPPT algorithms significantly enhances the reliability and efficiency of PV systems, paving the way for broader adoption of solar energy technologies in diverse environmental conditions. These findings contribute to advancing renewable energy technology and support the green energy transition.
Keywords
Photovoltaic Systems; Partial Shading Conditions; Maximum Power Point Tracking; 14 Artificial Neural Networks; Fuzzy Logic Control; GreyWolf Optimization; Particle Swarm 15 Optimization
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
