Dynamic and Statistical Analysis of a Three-Phase Induction Motor Under Stator Resistance and Rotor Inertia Variations Using MATLAB/Simulink

Abstract

Three-phase induction motors are widely used in industrial applications, and they are the backbone and driving force of modern manufacturing and production lines, owing to their robustness, reliability, and low maintenance requirements. However, variations in machine parameters such as electrical or mechanical parameters caused by thermal effects, machine lifespan or tolerances, operational conditions, or aging could significantly affect dynamic performance. This study examined the effects of varying stator resistance and rotor inertia using dynamic and statistical investigation of a three-phase induction motor in MATLAB/Simulink, analysed in the stationary reference frame. The dynamic performance was evaluated based on peak starting current, peak electromagnetic torque, settling time, and steady-state speed, under thirteen and twelve parameter variation points for stator resistance and rotor inertia, respectively, at an interval of 10% of the nominal value. The simulation results show that motor starting characteristics are greatly influenced by stator resistance. With a reduction of the stator resistance from 150% to 30% of its nominal value, the peak current and the peak torque increased from 408.84 A to 491.93 A and from 642.27 Nm to 981.40 Nm, respectively. A Pearson correlation statistical analysis revealed a very strong negative relationship between stator resistance and peak current (r =-0.9890, R2 = 0.9781) and peak torque (r =-0.9819, R2 = 0.9641). However, rotor inertia showed a very high positive correlation with peak torque (r = 0.9478; R2 = 0.8984). The effect of rotor inertia on settling time and peak current was not statistically significant. This result clearly shows that the stator resistance exhibited the strongest effect on the startup current and electromagnetic torque. The transient torque demand is mainly determined by the rotor inertia. The combined use of statistical correlation analysis and conventional dynamic simulation provides a quantitative methodology for the assessment of parameter sensitivity in induction motors and gives useful insight for machine design, condition monitoring, performance evaluation, and predictive maintenance applications.