Dynamic Simulation and Analysis of Three Phase Induction Motor for Faults Detection using Matlab/Simulink

Abstract

The dynamic simulation of three-phase induction motors under fault conditions is essential for understanding and mitigating the impacts of electrical faults on motor performance. This study aims to simulate and analyze the impact of electrical faults on three-phase induction motors to improve fault detection and isolation strategies. Utilizing MATLAB/Simulink software, the behavior of three-phase induction motors under both symmetrical and unsymmetrical faults is modeled and analyzed. The motor’s baseline parameters, include 3 Amps rated power and speed of 1500 RPM. Symmetrical faults, such as line-to-line-to-line (L-L-L), and unsymmetrical faults, like single-phase to ground faults, were simulated to observe their effects on motor operation. The d-q model was used to simulate motor dynamics, employing a block model approach to resolve reference frame theory issues. Major parameters analyzed include rotor speed, electromagnetic torque, and stator current. Through detailed simulations, key performance indicators such as torque fluctuations, current spikes, and decreases in rotor speed are examined. At 1.5 seconds, when the fault was introduced, the rotor speed, electromagnetic torque, and stator current were all affected. For instance, during a symmetrical fault, the rotor speed dropped from 1500 RPM to 1200 RPM, electromagnetic torque declined to -12 Nm, and the stator current increased to 7 Amps from the rated 3 Amps. Under an unsymmetrical single-phase-to-ground fault at the same instant, rotor speed decreased from 1500 RPM to 1400 RPM, electromagnetic torque declined to -12 Nm with greater distortion, and the stator current in the affected phases rose to 7 Amps from the rated 3 Amps. These results underscore the importance of robust fault detection and isolation mechanisms to enhance motor reliability and longevity. This work significantly contributes to engineering by offering validated simulation models and insights into parameter sensitivity, serving as both an educational resource and a foundation for advanced fault detection system development. The findings are applicable in academic research and industrial contexts, providing guidance for improving motor design and fault management strategies.