Parameter Estimation of Three-Phase Induction Motor Using Hybrid of Genetic Algorithm and Particle Swarm Optimization

Hamid Reza Mohammadi,Ali Akhavan

doi:10.1155/2014/148204

Abstract

A cost effective off-line method for equivalent circuit parameter estimation of an induction motor using hybrid of genetic algorithm and particle swarm optimization (HGAPSO) is proposed. The HGAPSO inherits the advantages of both genetic algorithm (GA) and particle swarm optimization (PSO). 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 nameplate data or experimental tests. In this paper, the problem formulation uses the starting torque, the full load torque, the maximum torque, and the full load power factor which are normally available from the manufacturer data. The proposed method is used to estimate the stator and rotor resistances, the stator and rotor leakage reactances, and the magnetizing reactance in the steady-state equivalent circuit. The optimization problem is formulated to minimize an objective function containing the error between the estimated and the manufacturer data. The validity of the proposed method is demonstrated for a preset model of induction motor in MATLAB/Simulink. Also, the performance evaluation of the proposed method is carried out by comparison between the results of the HGAPSO, GA, and PSO.

Highlights

In most of the applications, induction motors are preferred to DC motors, because of their simple structure, easy operation, and low cost maintenance and durability
To validate the proposed HGAPSO method for parameter estimation, this algorithm is tested on a 5 HP three-phase induction motor in MATLAB/Simulink preset models
The results show that all of the parameters obtained using HGAPSO are closer to original parameters and the HGAPSO has lesser error than genetic algorithm (GA) and particle swarm optimization (PSO)

Summary

Introduction

In most of the applications, induction motors are preferred to DC motors, because of their simple structure, easy operation, and low cost maintenance and durability. The information regarding motor circuit parameters is very important for design, performance evaluation, and feasibility of these control techniques. The conventional techniques for estimating the induction motor parameters are based on the locked-rotor and the noload tests. In the motors with high power, this test is impractical. These problems have encouraged the researchers to investigate alternative techniques for parameter estimation. The problem of induction motor parameter estimation has been addressed extensively by many researchers in the past. Parameter estimation of an induction motor using some evolutionary algorithms is presented in [5,6,7]

Methods

Results

Conclusion