Abstract
A Particle Swarm Optimization (PSO) based design of three-phase induction motors are proposed. The induction motor design is treated as a non-linear and multivariable constrained optimization problem. The annual material cost and the total annual cost of the motor are chosen as two different objective functions. The PSO is used to find a set of optimal design variables of the motor which are then used to predict performance indices and the objective functions. The proposed method is demonstrated for two sample motors, and it is compared with the genetic algorithm (GA) and the conventional design methods. The results show that the PSO-based method effectively solved the induction motor design problems and outperforms the other methods in both the solution quality and computation efficiency.
Highlights
The ever increasing imbalance between the demand and supply of energy has focused our attention towards energy conservation
The results show that the Particle Swarm Optimization (PSO)-based method effectively solved the induction motor design problems and outperforms the other methods in both the solution quality and computation efficiency
The method is applied on two sample motors and the results are compared with the genetic algorithm (GA) and the conventional design method [16,17]
Summary
The ever increasing imbalance between the demand and supply of energy has focused our attention towards energy conservation. Any significant improvement in the operating efficiency of induction motor will, help our effort at energy conservation This can be achieved by taking recourse to design optimization techniques. The objective function, in the form of a weighted sum of the material and operating costs, represents the consumer’s viewpoint [4,5,6] Several techniques such as GA [7], evolutionary algorithm [8], neural networks [9] and fuzzy logic [10] are used to solve the induction motor design problems. PSO is a population-based search algorithm characterized as conceptually simple, easy to implement and computationally efficient As it is reported in [13], this optimization technique can be used to solve many problems as GA and does not suffer from some of GA’s difficulties. The method is applied on two sample motors and the results are compared with the GA and the conventional design method [16,17]
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