An improved 2-level MPPT scheme for photovoltaic systems using a novel high-frequency learning based adjustable...

Area of Science:

• Electrical Engineering
• Renewable Energy Systems
• Control Theory

Background:

• Model Reference Adaptive Control (MRAC) for Maximum Power Point Tracking (MPPT) requires high adaptation gain for fast response.
• High adaptation gain in MPPT can lead to control signal oscillations, instability, and inefficient operation, especially under dynamic environmental conditions.

Purpose of the Study:

• To propose a novel High-Frequency Learning-based Adjustable Gain MRAC (HFLAG-MRAC) for a two-level MPPT architecture in photovoltaic (PV) systems.
• To ensure maximum power delivery under rapidly changing environmental conditions while maintaining system stability and efficiency.

Main Methods:

• A two-level MPPT control architecture combining conventional Ripple Correlation Control (RCC) with the proposed HFLAG-MRAC.
• The HFLAG-MRAC features an adaptive law where the adaptation gain is adjusted based on the high-frequency ripple content of the tracking error, derived using Lyapunov theory.

Main Results:

• The proposed HFLAG-MRAC achieves fast convergence and guaranteed transient performance without requiring high adaptation gain.
• The adaptive gain adjustment effectively mitigates high-frequency oscillations, ensuring numerical stability and efficient PV system operation.

Conclusions:

• The HFLAG-MRAC effectively addresses the limitations of conventional MRAC-based MPPT schemes under dynamic environmental conditions.
• Simulation and experimental results validate the proposed method's superior performance and stability compared to existing approaches.