Multi-Objective Optimization Technique Based Design of Squirrel Cage Induction Motor

Induction motors are widely used in industries because of their rugged construction and simple operation. Owing to their relatively low cost, reliability and efficiency, 80% of the electrical motors are the three-phase squirrel cage induction motors (SCIMs). In most industries, they are the main energy consuming devices, contributing to more than 80% of electromechanical energy consumption. The objective of the present research is to develop novel parameter optimization technique by applying soft computing algorithms such as Genetic Algorithm (GA) and Particle Swarm optimization (PSO) for optimal design of different types of induction. It will not only improve the efficiency of motor but also improve the power factor. Here two objectives are taken simultaneously for the optimization, but the involvement of two mutually contradictory objective functions makes the current optimization problem as multi-objective one. The multi-objective optimization problem is converted into single objective problem by giving different weights to each objective according to the priority of the objective functions. The results obtained from the optimization are the non-dominated solutions, which forms a pareto curve. Every point in the pareto curve is a solution for the problem, we have to select one solution depending upon our requirements.The design parameters obtained from the conventional design technique and the performance of all the proposed techniques are compared. The improvement in performance is achieved by reducing the variable losses occurred in the motor by suitable design. To validate the objective of research, different stator and rotor winding configurations have been attempted on 7.5 kW three phase Squirrel Cage Induction Motor (SCIM).