Advanced MPPT Tracking for PV Systems Using Model Predictive Control in Modified SEPIC Topology

Abstract

With fewer sensors needed, this research aims to reduce hardware costs while still achieving maximum power point tracking (MPPT) in photovoltaic (PV) systems. Using a modified SEPIC converter and an MPPT algorithm based on model predictive control (MPC), the approach aspires to provide efficient MPPT under different environmental situations. The suggested method uses a single voltage and current sensor to accomplish the goal, which drastically reduces the hardware needs in comparison to conventional MPPT methods. To control the current and voltage in the PV system, the modified SEPIC converter is used. The converter's operation is adjusted dynamically to monitor the maximum power point using the MPC-based MPPT algorithm. Optimal power production may be predicted and optimized using the algorithm's model predictive control technique, which makes use of a PV system predictive model. For precise MPPT, the algorithm uses the measured data from the sensors to foretell how the PV system would operate. In order to optimize power extraction, the program makes modifications in real-time. This research shows that the suggested method reduces hardware cost by successfully tracking the PV system's highest power point using only one voltage and one current sensor. Under different operating circumstances, the updated SEPIC converter and the MPC-based MPPT algorithm efficiently extract power. The suggested method is more efficient and cost-effective than conventional MPPT methods, according to the simulation and experimental findings.