An advanced direct torque control for doubly fed induction motor using evolutionary computational techniques.

Area of Science:

• Electrical Engineering
• Control Systems
• Renewable Energy Systems

Background:

• Doubly-fed induction machines (DFIMs) offer superior efficiency in variable-speed applications compared to cage machines.
• DFIMs require advanced control strategies like Direct Torque Control (DTC) due to complex dynamics.
• Standard DTC with PID controllers can lead to torque ripples and speed overshoot.

Purpose of the Study:

• To develop and evaluate optimized DTC strategies for DFIM speed control.
• To address performance limitations of conventional PID-based DTC.
• To improve DFIM stability and reduce torque ripples under disturbances.

Main Methods:

• Integration of a PID controller with DTC for DFIM speed control.
• Application of Genetic Algorithm (GA) and Ant Colony Optimization (ACO) for PID parameter tuning.
• Simulation-based performance analysis of GA-DTC and ACO-DTC methods.

Main Results:

• Both GA-DTC and ACO-DTC hybrid controls demonstrate significant performance improvements.
• ACO-DTC achieved a 27.86% reduction in torque ripples compared to baseline methods.
• Optimized control strategies enhance system stability and potentially extend machine operational life.

Conclusions:

• Hybrid GA-DTC and ACO-DTC offer effective solutions for DFIM speed control.
• ACO-DTC provides superior performance in reducing torque ripples and improving stability.
• These optimized control methods show strong potential for industrial DFIM applications.