An adapted model predictive control MPPT for validation of optimum GMPP tracking under partial shading conditions

Area of Science:

• Renewable Energy Systems
• Electrical Engineering
• Control Systems

Background:

• Partial shading conditions (PSCs) significantly reduce photovoltaic (PV) system efficiency by causing multiple local maximum power points (LMPPs).
• Accurate tracking of the global maximum power point (GMPP) is crucial for maximizing energy yield in PV systems under varying irradiance.

Purpose of the Study:

• To propose an adapted hybrid control algorithm for robust global maximum power point tracking (GMPPT) in PV systems under partial shading.
• To enhance the energy generation efficiency and cost-effectiveness of PV systems by overcoming the limitations of traditional tracking methods.

Main Methods:

• Implementation of a 3x3 multi-string PV array with a modified boost converter.
• Utilizing an adapted perturb and observe-based model predictive control (APO-MPC) algorithm for GMPPT.
• Predictive control strategy to stabilize output power and track GMPP by minimizing the MPC cost function.

Main Results:

• The proposed APO-MPC algorithm successfully tracks the GMPP, preventing convergence to local maxima under various PSCs.
• The system demonstrates accurate and stable convergence to the GMPP with minimal sensors, reducing hardware costs.
• The method outperforms classical techniques in cost-effectiveness and power extraction efficiency under constant, changing, and linearly changing irradiances.

Conclusions:

• The developed adapted hybrid control algorithm offers a cost-effective and efficient solution for GMPPT in PV systems facing partial shading.
• The sensorless approach and stable tracking performance highlight the practical applicability and advantages of the proposed method.