Enhancing the backstepping control approach competencies for wind turbine systems using a dual star induction...

Area of Science:

• Electrical Engineering
• Renewable Energy Systems
• Control Theory

Background:

• Wind energy conversion systems often utilize dual-star induction generators (DSIGs).
• Effective control strategies are crucial for optimizing DSIG performance and grid integration.
• Existing control methods may face challenges with dynamic response and parameter variations.

Purpose of the Study:

• To introduce a novel and improved backstepping control (BC) technique for DSIGs powered by wind turbines.
• To enhance the robustness and dynamic performance of the DSIG using the ant lion optimization (ALO) algorithm.
• To validate the effectiveness of the proposed BC-ALO approach through simulations.

Main Methods:

• The study employs a backstepping control (BC) strategy optimized by the ant lion optimization (ALO) algorithm.
• ALO is used to determine optimal parameters for the BC controller, integrating Integral Time Absolute Error (ITAE) and Integral Time Squared Error (ITSE) criteria.
• Simulations are conducted in MATLAB to evaluate the performance of the BC-ALO approach.

Main Results:

• The BC-ALO approach significantly reduces total harmonic distortion (THD) by up to 50.44% compared to the traditional BC method.
• It improves DSIG power overshoot by an estimated 100% and enhances reactive power response time by over 95%.
• The method effectively minimizes DC link voltage ripples (up to 71.89%) and demonstrates robustness against model parameter uncertainties.

Conclusions:

• The proposed BC-ALO technique offers superior performance for DSIG-based wind energy systems compared to conventional BC.
• The optimization via ALO leads to enhanced dynamic response, reduced electrical distortions, and improved stability.
• The approach's versatility suggests potential applications in other renewable energy systems, such as photovoltaic systems.