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@article{168173,
        author = {Mohd Talha Zubair and DR K.H Phanishree},
        title = {Enhancing the Efficiency of Grid Connected Bifacial PV System using Artificial Neural Network},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {5},
        pages = {98-104},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168173},
        abstract = {To meet the world's growing energy demands, countries are increasingly promoting the installation of photovoltaic (PV) systems to reduce reliance on fossil fuel imports. However, conventional monofacial PV modules typically achieve only 20-25% efficiency, which poses challenges for maximizing power output. In contrast, advanced bifacial PV panels can capture sunlight from both sides, enhancing the overall efficiency of the system. It is crucial to operate PV systems at their maximum power point (MPP) to optimize performance. This paper proposes an advanced neural network-based maximum power point tracking (MPPT) system for bifacial PV systems. By integrating the advantages of bifacial PV modules with artificial neural networks, this approach aims to address the challenges associated with solar energy utilization effectively. To evaluate the effectiveness of the proposed method, it was compared with a monofacial PV module and the traditional Perturb and Observe algorithm. The results indicate that the proposed method achieves higher output power, greater tracking efficiency, lower total harmonic distortion (THD), and reduced settling time.},
        keywords = {PV, Bifacial PV module, MPPT, ANN},
        month = {October},
        }