COST OPTIMAL DESIGN OF INDUCTION MOTOR BY GENETIC ALGORITHM
RAJU BASAK *
Postdoctoral Research Scholar, GEP Department, Université Claude Bernard Lyon 1, France.
HAMED YAHOUI
Laboratoire AMPERE, Université Claude Bernard Lyon 1, France
NICOLAS SIAUVE
Laboratoire AMPERE, Université Claude Bernard Lyon 1, France.
*Author to whom correspondence should be addressed.
Abstract
Induction motors find its application in almost every industry and their cost optimal design is important from a manufacturing point of view because it has a wide field of applications and its cost is a sizable proportion of total costs. While designing induction motors it is important to keep in mind their cost and performance. In this paper, an attempt has been made to cost optimal design of a 15 kW, 440 v Squirrel cage Induction motor by framing an objective function of cost. The function is optimized by the Genetic algorithm. The objective function is the sum of the cost of iron and copper and it is being optimized under some constraints chosen on the basis of performance. The first step is to choose the design variables, which affect the cost of the motor and then the function is developed step by step. Optimal design parameters are well accepted in case of job production, but the stampings are needed to manufacture within the unit itself in the case of mass production. The constraints are applied to achieve the performance above a certain level. Finally, the result is compared and validated by the process of Simulated Annealing.
Keywords: Squirrel cage induction motor, genetic algorithm, objective function, constraints