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@article{188040,
        author = {MOHANRAM GNANASEKARAN and Dr. Bhavani M},
        title = {A THREE PHASE GRID-TIED SOLAR PHOTOVOLTAIC SYSTEM WITH IMPROVED POWER PROFILE USING MODULAR PROBABILISTIC NEURAL NETWORK},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {2091-2099},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188040},
        abstract = {The increasing use of photovoltaics (PV) as a green energy resource has surged in recent years, primarily due to their integration with traditional power systems, which helps meet global energy needs and reduce carbon emissions. However, generating green electricity from this renewable source is often susceptible to power quality (PQ) disruptions caused by the intermittent nature of PV systems and other factors related to the electric grid, power converters, and connected loads. These disruptions must be minimized to prevent deterioration of the PQ in the studied system, which includes PV, DC-DC, and DC-AC converters, filters, the power grid, and control schemes. Without proper management of the DC-DC converter, deviations from the maximum power point (MPP) of the PV system will occur. To maximize the energy harvested from the PV system, this research developed MPP tracking (MPPT) algorithms using Modular Probabilistic Neural Network (MPNN) to adjusts the VDC reference signal of the inverter VDC regulator to achieve maximum power extraction from the PV array. Simulation results demonstrated that MPNN outperformed in tracking maximum power.},
        keywords = {Renewable source, Probabilistic Neural Network, Modular Probabilistic Neural Network, Maximum power point Tracking.},
        month = {December},
        }