International Journal of Research in Advanced Electronics Engineering
2025, Vol. 6, Issue 1, Part A
Analysis of PV with MPPT systems using ANN multi-plug (MLP) and Radial Basis Functions (RBF)
Author(s): Ahmed S Rahi
Abstract: The growing energy demand due to industrial expansion necessitates efficient energy production and consumption strategies. This study provides a comprehensive analysis of photovoltaic (PV) maximum power point tracking (MPPT) systems utilizing artificial neural networks (ANNs) under dynamic environmental conditions. ANNs, such as Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF), significantly enhance PV system performance by optimizing energy extraction and improving adaptability to fluctuating temperatures and radiation. The research highlights the superior response times and power output of ANN-based MPPT systems compared to traditional methods like Perturb and Observe (P&O), while addressing challenges such as computational demands and the need for diverse, high-quality training data. The findings emphasize the potential of ANN-driven MPPT systems as a robust solution for renewable energy optimization. Future directions involve integrating deep learning and leveraging real-world data to further enhance the efficiency and adaptability of PV systems in varying climates.
DOI: 10.22271/27084558.2025.v6.i1a.47
Pages: 01-15 | Views: 98 | Downloads: 35
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How to cite this article:
Ahmed S Rahi. Analysis of PV with MPPT systems using ANN multi-plug (MLP) and Radial Basis Functions (RBF). Int J Res Adv Electron Eng 2025;6(1):01-15. DOI: 10.22271/27084558.2025.v6.i1a.47
