An Optimization of Design for S4-Duty Induction Motor Using Constraints Normalization Based Violation Technique

Abstract

Problem statement: Design of Induction motors is an engineering art and needs an extensive experience for obtaining an optimal design solution for a given design problem. An optimized GD² value for the development of S4 Duty, cage induction motor to meet the specifications of a particular designated application (the location of application is kept as trade secret due to IP barrier) is proposed and validated by a physical model. Approach: The Genetic Algorithm (GA) is used to optimize inertia of rotating member as a single objective function for the designated application. A formulation based on the violations of normalized constraints is used here to transform the problem as unconstrained one. Results: The design variables for the developed model for the rating of 30 W for S4 duty operation were examined with the GA operators such as Initial population-15; two point crossover probability-0.8; mutation-0.05; number of generations-50; fitness scaling-rank; selection-Roulette wheel; Conclusion: The GD² value obtained using the GA-constraints normalization technique and from the proto model developed are 26.24 and 33.04 respectively as against the specified value 40.75 kg-cm².