Enhancing MPPT optimization with hybrid predictive control and adaptive P&O for better efficiency and power quality...

Area of Science:

• Renewable Energy Systems
• Power Electronics
• Control Systems

Background:

• Grid-connected solar power systems are crucial for energy demand and climate change mitigation.
• However, power quality and stability remain significant challenges in these systems.
• Existing maximum power point tracking (MPPT) methods, like perturb and observe (P&O), show limitations in performance and durability.

Purpose of the Study:

• To introduce a novel MPPT strategy for photovoltaic inverters.
• The goal is to enhance power quality, system stability, and dynamic response.
• To address the shortcomings of conventional MPPT algorithms.

Main Methods:

• A new MPPT strategy integrating modified finite control set model predictive control (MFCS-MPC) with an adaptive P&O algorithm was developed.
• A comprehensive mathematical model of the photovoltaic system was built and validated using MATLAB/Simulink.
• Performance was evaluated under various operating conditions.

Main Results:

• The proposed MPPT approach achieved a total harmonic distortion (THD) of 1.22%, significantly better than the conventional P&O algorithm's 6% and meeting IEEE-519 standards.
• Tracking response time improved by 35%, and overshoot was reduced by 28%, ensuring stability during rapid solar radiation changes.
• The MFCS-MPC's weighted cost function reduced switching losses by 15% while maintaining harmonic suppression.

Conclusions:

• The developed MPPT strategy offers superior robustness, operational performance, and dynamic response compared to conventional methods.
• It effectively improves power quality, stability, and efficiency in photovoltaic systems.
• The approach shows promise for broader applications in renewable energy and electric vehicles.