Enhancement of wind energy conversion system performance using adaptive fractional order PI blade angle controller.

Area of Science:

• Renewable Energy Systems
• Control Systems Engineering
• Fractional Calculus Applications

Background:

• Wind energy is a rapidly growing renewable energy source.
• Effective control systems are crucial for optimizing wind turbine performance and stability.
• Traditional controllers like PID may face limitations in highly variable conditions.

Purpose of the Study:

• To enhance wind energy performance using a novel adaptive fractional order PI (AFOPI) blade angle controller.
• To optimize controller parameters and integrator order for improved efficiency.
• To validate the robustness and effectiveness of the AFOPI controller.

Main Methods:

• Development of an adaptive fractional order PI (AFOPI) controller utilizing fractional calculus.
• Optimization of controller parameters and integrator order via Harmony Search Algorithm (HSA) and Equilibrium Optimization (EO) hybrid.
• Auto-tuning of controller gains.
• Comparative analysis against traditional PID and Adaptive PI controllers under normal and fault conditions.
• Testing under high wind speed variation profiles.

Main Results:

• The AFOPI controller significantly improved wind turbine electrical and mechanical behaviors.
• Demonstrated robustness against wind speed variations and system nonlinearity.
• Ensured continuous wind power generation even during sharp wind speed changes.
• Achieved approximately 25% increase in captured energy.
• Reduced standard deviation and root mean square error by approximately 10%.

Conclusions:

• The proposed AFOPI controller offers superior performance in enhancing wind energy generation.
• The controller exhibits significant robustness and adaptability to dynamic system conditions.
• AFOPI represents a promising advancement for optimizing wind turbine control and energy capture.