Particle swarm optimization of synergetic controller and sliding-mode extreme seeking controller for wind power...

Area of Science:

• Electrical Engineering
• Control Systems
• Renewable Energy Systems

Background:

• Doubly Fed Induction Generators (DFIGs) are crucial for wind energy conversion.
• Conventional control methods like Field-Oriented Control (FOC) with PI regulators face limitations in performance and robustness.
• Enhancing DFIG control is vital for maximizing wind power generation and grid integration.

Purpose of the Study:

• To propose a novel robust control strategy for DFIG-based wind turbines.
• To integrate Synergetic Control (SC) with Sliding Mode Extremum Seeking Control (SMESC) for enhanced performance.
• To optimize controller parameters using Particle Swarm Optimization (PSO).

Main Methods:

• A dual-loop control architecture combining SC for power regulation and SMESC for Maximum Power Point Tracking (MPPT).
• Replacement of conventional FOC-PI regulators with a systematic SC-SMESC formulation.
• Parameter optimization via Particle Swarm Optimization (PSO) algorithm.

Main Results:

• Improved MPPT efficiency by 2%.
• Reduced steady-state error by 77.52% and settling time by 96.15%.
• Lowered Total Harmonic Distortion (THD) of stator currents to 0.63%.

Conclusions:

• The proposed SC-SMESC strategy offers superior performance and reliability for DFIG wind turbines.
• The control scheme demonstrates strong robustness against parameter variations and external disturbances.
• This approach is effective for high-performance wind energy conversion systems.