Robust Adaptive HCS MPPT Algorithm-Based Wind Generation System Using Model Reference Adaptive Control

Area of Science:

• Renewable Energy Systems
• Control Engineering
• Electrical Engineering

Background:

• Wind energy conversion systems (WECS) require efficient maximum power point tracking (MPPT) to capture available energy.
• Conventional hill-climbing search (HCS) MPPT techniques face challenges with step-size selection, impacting dynamic performance.
• Limitations in existing HCS methods lead to suboptimal tracking speed, power fluctuations, and reduced system efficiency.

Purpose of the Study:

• To propose a novel adaptive hill-climbing search (AD-HCS) technique for enhanced MPPT in WECS.
• To address the limitations of fixed step-size selection in traditional HCS methods.
• To improve the overall dynamic performance and efficiency of WECS.

Main Methods:

• Developed an AD-HCS technique utilizing model reference adaptive control (MRAC) with a PID controller.
• Implemented a self-adjustable step-size mechanism based on detected mechanical power fluctuations.
• Optimized PID gains dynamically to achieve optimal tracking conditions and harvest maximum power.

Main Results:

• The proposed AD-HCS technique demonstrated reduced oscillations around the maximum power point (MPP).
• Achieved a smaller settling time compared to conventional and recent HCS techniques.
• Reported a 5% and 2% increase in WECS efficiency over conventional and recent HCS methods, respectively.

Conclusions:

• The AD-HCS technique effectively enhances MPPT performance in WECS.
• The MRAC-based adaptive step-size control offers superior tracking accuracy and efficiency.
• Validated the AD-HCS technique on a 1.5 MW grid-tied DFIG-based WECS using MATLAB/Simulink.