Optimal design of induction motor for a spinning machine using population based metaheuristics

This paper deals with the design optimization of a squirrel-cage three-phase induction motor, selected as the driving power of spinning machine in textile industry, using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Efficiency, which decides the operating or running cost of the motor (industry), is considered as objective function. First, the algorithms are applied to design a general purpose motor with seven variables and nine performance related parameters with their nominal values as constraints. To make the machine feasible, practically acceptable to serve in textile industries and less operating cost, certain constraints are modified in accordance with the demands in spinning application. Comparison of the optimum designs with the industrial (existing) motor reveals that the motor designed for textile load diagram consumes less power input. Economical analysis is also given.