Maximum Power Point Tracking-Based Model Predictive Control for Photovoltaic Systems: Investigation and New...

Area of Science:

• Electrical Engineering
• Renewable Energy Systems
• Control Systems

Background:

• Maximum Power Point Tracking (MPPT) is crucial for optimizing photovoltaic (PV) energy generation.
• Model Predictive Control (MPC) offers advanced strategies for MPPT implementation.
• Existing MPPT-based MPC methods include fixed and variable switching techniques, each with implementation complexities.

Purpose of the Study:

• To present a comparative review of MPPT techniques utilizing Model Predictive Control (MPC).
• To introduce novel, simplified MPC-based MPPT methods.
• To evaluate the performance of conventional and proposed techniques under various operating conditions.

Main Methods:

• Comparative review of fixed and variable switching MPPT techniques based on MPC.
• Development of a simplified fixed switching MPC method by eliminating the Proportional-Integral (PI) controller.
• Proposal of a direct realization technique for variable switching MPC, avoiding converter model discretization.
• Experimental validation under static and dynamic conditions.

Main Results:

• The proposed methods simplify the implementation of MPPT-based MPC.
• Elimination of the PI controller in fixed switching methods enhances efficiency.
• Direct realization of variable switching MPC simplifies application to diverse converters.
• Reduced sensor count achieved with proposed techniques.
• Experimental results demonstrate effectiveness under varying conditions.

Conclusions:

• Simplified MPC-based MPPT techniques offer improved performance and easier implementation for PV systems.
• The proposed methods reduce hardware requirements and enhance control efficiency.
• These advancements contribute to more effective utilization of solar energy.