Abstract
The integration of photovoltaic (PV) systems into the power grid has gained considerable attention due to the increasing demand for renewable energy sources. A critical aspect of grid-connected PV systems is the efficient extraction of maximum power from the PV modules under varying environmental conditions. Classical Maximum Power Point Tracking (MPPT) techniques, such as Perturb and Observe (PO) and Incremental Conductance (IC), often struggle with slow convergence and reduced accuracy under rapid changes in irradiance and temperature. To address these limitations, this study proposes the use of an advanced MPPT algorithm based on Artificial Neural Network (ANN), which offers improved tracking efficiency and faster response to dynamic conditions. The proposed MPPT method is implemented and tested within a grid-connected PV system using a three-level Neutral Point Clamped (NPC) inverter for power conversion. The NPC inverter is chosen for its advantages in reducing harmonic distortion and improving voltage quality compared to traditional two-level inverters. A detailed model of the PV system, including the inverter, is developed in MATLAB/Simulink to evaluate the effectiveness of the advanced MPPT method. Various scenarios are simulated to examine the system's performance under fluctuating irradiance changes. This study concludes that the integration of advanced MPPT algorithms with sophisticated inverter topologies is a viable solution for optimizing the performance of grid-connected PV systems. Future research will focus on validating the proposed approach in real-time environments and exploring hybrid control strategies that integrate energy storage systems.
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