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@article{150436,
        author = {Prakash Prajapat and Usha Sharma and Suresh Meghwal},
        title = {Neural Network MPPT control for Grid-Connected PV System},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {7},
        number = {6},
        pages = {88-93},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=150436},
        abstract = {In this paper, the neural network (NN) based maximum power point tracking (MPPT) controller is presented for grid connected PV system. The grid connected PV system has its advantage of as it does not require storage system and provide efficient power utilization. The proposed system consists of PV array made from the combination of series parallel connected PV modules, boost converter on which MPPT is incorporated, three-phase voltage source inverter and the grid. Complete system is modeled and simulated using MATLAB/ Simulink and NN is trained using the neural network toolbox. To validate the performance of the proposed MPPT controller, comparative study is performed with conventional P&O MPPT controller. The study reveals that the proposed NN based MPPT controller shows better performance and produce satisfactory results. },
        keywords = {MPPT, ANN, Artificial intelligence, Solar Photovoltaic, P&O},
        month = {},
        }