TITLE:
Optimizing Star-Delta Starter Transitions for Induction Motors with Particle Swarm Algorithm
AUTHORS:
Jacob Owusu Ansah, Ernest Smith Mawuli, Enoch Boateng Adomako
KEYWORDS:
Closed Transition Star-Delta Starter, Induction Motor, Switching Transient Time, Particle Swarm Optimization Algorithm, Starting Current
JOURNAL NAME:
Intelligent Control and Automation,
Vol.17 No.1,
December
22,
2025
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.