Abstract
Induction motors are popularly used in various applications because of the proposed modest construction, substantiated process, and limited size of specific power. The traditional AC traction drives are experimentally analyzed. There is a high circulating current due to the high Common-Mode Voltage (CMV). The high Circulating Bearing Current (CBC) is a major problem in conventional two-level voltage source inverter fed parallel-connected sensor-based induction motors for traction applications. A sensorless method is well known for shrinking costs and enhancing the reliability of an induction motor drive. The modified artificial neural network-based model reference adaptive system is designed to realize speed estimation methods for the sensorless drive. Four dissimilar multilevel inverter network topologies are being implemented to reduce CBC in the proposed sensorless traction motor drives. The multilevel inverter types are T-bridge, Neutral Point Clamped Inverter (NPC), cascaded H-bridge, and modified reduced switch topologies. The four methods are compared, and the best method has been identified in terms of 80% less CMV compared to the conventional one. The modified cascaded H-bridge inverter reduces the CBC of the proposed artificial neural network-based parallel connected induction motor; it is 50% compared to the conventional method. The CBC of the modified method is analyzed and associated with the traditional method. Finally, the parallel-connected induction motor traction drive hardware is implemented, and the performance is analyzed.