An AIAPO MPPT controller based real time adaptive maximum power point tracking technique for wind turbine system

Area of Science:

• Renewable Energy Systems
• Artificial Intelligence in Engineering
• Control Systems

Background:

• Growing global energy demand necessitates sustainable solutions.
• Conventional energy sources pose environmental challenges.
• Renewable energy (RE) is crucial for power generation.

Purpose of the Study:

• To propose a novel Artificial Intelligence Based Adaptive P&O (AIAPO) for real-time adaptive hybrid Maximum Power Point Tracking (MPPT).
• To enhance the controller design mathematically, overcoming limitations of conventional MPPT and fuzzy logic (FL) controllers.
• To improve the efficiency and tracking performance of Wind Turbine (WT) systems.

Main Methods:

• Developed an AIAPO controller integrating FL for optimum perturbation calculation based on wind speed (WS) variations.
• Implemented the optimum perturbation within an adaptive P&O technique to generate a desirable duty cycle for a DC-DC converter.
• Compared real-time outcomes with existing perturb & observe (P&O) and fuzzy logic (FL) MPPT techniques for Wind Turbine Induction Generator (WTIG) systems.

Main Results:

• The proposed AIAPO controller demonstrated improved power tracking by reducing steady-state fluctuations and enhancing transient performance.
• Statistical analysis showed superior performance compared to P&O, FL, and SVM techniques.
• Achieved a best value of 230.5365, worst value of 210.5934, mean value of 230.952, and standard deviation of 0.05314.

Conclusions:

• The AIAPO controller offers a significant advancement in MPPT for WT systems.
• The method effectively enhances power tracking efficiency and stability under variable wind conditions.
• This AI-driven approach provides a robust and efficient solution for renewable energy integration.