Bio-inspired Multi-objective Optimization Design of a Highly Efficient Squirrel Cage Induction Motor

Abstract

Although three-phase squirrel cage induction motors are commonly considered a mature technology, their design has always been a challenge for engineering; therefore, new techniques and methodologies are continually being proposed. The growth in the relevance of efficiency has set it as part of the design objectives. However, increased efficiency is in conflict with the manufacturing cost, not only because of the cost of improved materials, but also because of the dimensional modifications. Therefore, a true multi-objective optimization methodology turns out to be attractive for this engineering problem. On the other hand, bio-inspired methods have become an important tool for induction motor optimal design because this involves many variables and parameters, and it is in general a complex optimization problem by nature. Thus, a methodology for the design of highly efficient three-phase squirrel cage induction motors, based on the non-dominated sorting genetic algorithm II (NSGA-II) and the non-dominated sorting particle swarm optimization (NSPSO), together with a true multi-objective optimization problem of manufacturing cost and operation efficiency, is proposed in this paper. The methods are evaluated and compared in order to be used with this type of engineering problems.