Optimization Techniques in Parameters Setting for Induction Motor

Abstract

The continuous increasing electrical energy consumption is a critical condition nowadays, so that, many research fields have increased efforts to solve such problems. The induction motors are the most electrical energy consuming in industries; this high consumption is directly caused by their internal characteristic parameters which are not available. Under such circumstance, a parameter identification process must be handled; such a task can be treated as an optimization problem where the internal parameters of induction motors are considered as decision variables. From the optimization problem point of view, the parameter estimation of the induction motors is a complex task due to its non-linear nature where the error surface generated is highly multimodal. Several optimization techniques reported in the literature have been used to parameter determination of induction motors. Nevertheless, most of them have certain limitations such as their poor balance between exploration and exploitation producing sub-optimal solutions. In this chapter, an optimization technique is presented. This technique uses operators that allow a better equilibrium between exploration-exploitation avoiding the local minima in multimodal problems. The presented technique is a recent approach called Gravitational Search Algorithms (GSA). Several experimental tests have been performed to evaluate its accuracy.