Optimizing Star-Delta Starter Transitions for Induction Motors with Particle Swarm Algorithm

Abstract

Induction motors are widely employed in industrial applications due to their robustness, speed consistency, and cost efficiency. However, during startup, they draw a high inrush current that distorts electromagnetic torque and speed, causing overheating, prolonged transient periods, and noise. To address this, the closed transition star-delta starter (CTSDS) is often used as a baseline technique. This study proposes a Modified Particle Swarm Optimization (MPSO) algorithm to enhance CTSDS performance by optimally determining the transition parameters that minimize starting current and transient duration. A MATLAB/Simulink model was developed to analyze motor behavior under transient conditions, evaluating speed, torque, voltage, and current characteristics. The MPSO-based method was compared with conventional CTSDS and timer-based (TB) transition schemes. Results show that the proposed MPSO approach achieves 5.3 sec in settling time with only 0.4% voltage overshoot, outperforming TB (9.16 sec settling time, 30% overshoot) and CTSDS (21 sec settling time, 129.82% overshoot). The findings demonstrate that the MPSO-controlled starter enables faster and smoother transitions with significantly reduced switching transients and inrush currents, making it an efficient alternative for induction motor control systems.