AI‑Based Maximum Power Point Tracking (MPPT) for Solar PV Systems under Partial Shading

Authors

  • Digvijay Kumar,Prerna,Atish Kumar,Pritam Kumar

Keywords:

Maximum Power Point Tracking (MPPT), Partial Shading, Solar PV, Artificial Intelligence, ANFIS, Neural Networks, Fuzzy Logic.

Abstract

Partial shading conditions (PSC) critically impair the efficiency of solar photovoltaic (PV)systems by introducing multiple local maxima in the power-voltage (P-V) characteristic curve. Conventional Maximum Power Point Tracking (MPPT) techniques

References

Alajmi, B. N., Ahmed, K. H., Finney, S. J., & Williams, B. W. (2011). Fuzzy-logiccontrol approach of a modified hill-climbing method for maximum power point in microgrid standalone photovoltaic system. IEEE Transactions on Power Electronics, *26*(4), 1022–1030. https://doi.org/10.1109/TPEL.2010.2091503

Alajmi, B. N., Ahmed, K. H., Finney, S. J., & Williams, B. W. (2013). A maximum power point tracking technique for partially shaded photovoltaic systems in microgrids. IEEE Transactions on Industrial Electronics, *60*(4), 1596–1606. https://doi.org/10.1109/TIE.2011.2168796

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Published

2024-10-12

How to Cite

Digvijay Kumar,Prerna,Atish Kumar,Pritam Kumar. (2024). AI‑Based Maximum Power Point Tracking (MPPT) for Solar PV Systems under Partial Shading. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 8177–8191. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5232

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Section

Articles