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Multi-objective parameter estimation of induction motor using particle swarm optimization

Authors
Journal
Engineering Applications of Artificial Intelligence
0952-1976
Publisher
Elsevier
Volume
23
Issue
3
Identifiers
DOI: 10.1016/j.engappai.2009.06.004
Keywords
  • Genetic Algorithms
  • Induction Motor Parameter Estimation
  • Particle Swarm Optimization
  • Sensitivity Analysis
  • Deep Bar Circuit Model
Disciplines
  • Computer Science
  • Mathematics

Abstract

Abstract In order to simplify the offline parameter estimation of induction motor, a method based on optimization using a particle swarm optimization (PSO) technique is presented. Three different induction motor models such as approximate, exact and deep bar circuit models are considered. The parameter estimation methodology describes a method for estimating the steady-state equivalent circuit parameters from the motor performance characteristics, which is normally available from the manufacturer data or from tests. The optimization problem is formulated as multi-objective function to minimize the error between the estimated and the manufacturer data. The sensitivity analysis is also performed to identify parameters, which have the most impact on motor performance. The feasibility of the proposed method is demonstrated for two different motors and it is compared with the genetic algorithm and the classical parameter estimation method. Simulation results show that the proposed PSO method was indeed capable of estimating the parameters over a wide operating range of the motor.

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