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

The doubly-fed induction machine is progressively supplanting the cage machine owing to its superior efficiency in variable-speed applications and improved performance in renewable energy systems. Nonetheless, its complicated mathematical model, derived from the interdependent rotor and stator dynamics, necessitates more effective control solutions, such as direct torque control (DTC) in doubly-fed induction motor (DFIM) applications. DTC, particularly when integrated with a simple PID controller offers powerful and dynamic performance; yet, it may result in torque ripples owing to hysteresis control and speed overshoot from abrupt torque demand fluctuations. Moreover, careful fine-tuning of the PID controller parameters is necessary. This paper presents a methodology that integrates DTC-based PID controller with two optimization algorithms, with either Genetic algorithm (GA) or ant colony optimization (ACO). These optimization strategies are designed to optimally tune the PID controller settings for speed control improvements and to address internal and external disturbances. Simulation results show that the new hybrid GA-DTC and ACO-DTC controls significantly improve performance. In particular, ACO-DTC reduces torque ripples by 27.86%, improving stability and extending machine life. These methods offer promising prospects for the industrial application of doubly-fed induction motor control systems.