Enhancing Photovoltaic Conversion Efficiency With Model Predictive Control-Based Sensor-Reduced Maximum Power Point Tracking in Modified SEPIC Converters

The objective of this paper is to propose a new technique for maximum power point tracking (MPPT) in photovoltaic (PV) systems that utilizes fewer sensors, thereby reducing the hardware cost. The technique aims to achieve efficient MPPT under various environmental conditions by employing a modified SEPIC converter and a model predictive control (MPC)-based MPPT algorithm. To achieve the objective, the proposed technique utilizes only one voltage sensor and one current sensor, significantly reducing the hardware requirements compared to traditional MPPT techniques. The modified SEPIC converter is employed to regulate the voltage and current levels in the PV system. The MPC-based MPPT algorithm is implemented to dynamically adjust the operation of the converter and track the maximum power point. The algorithm incorporates a model predictive control approach, which utilizes a predictive model of the PV system to anticipate and optimize the power output. The algorithm predicts the behavior of the PV system based on the available sensor measurements, allowing for accurate MPPT. The algorithm operates in real-time, providing instantaneous adjustments to maximize power extraction. The study demonstrates that the proposed technique effectively tracks the maximum power point of the PV system using only one voltage sensor and one current sensor, thus reducing the overall hardware cost. The MPC-based MPPT algorithm, in combination with the modified SEPIC converter, achieves efficient power extraction under various operating conditions. The simulation and experimental results indicate that the proposed technique outperforms traditional MPPT techniques in terms of cost-effectiveness and power extraction efficiency.