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

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Hamid Reza Mohammadi ◽  
Ali Akhavan
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Steady State ◽  
Equivalent Circuit ◽  
Induction Motor ◽  
Particle Swarm ◽  
Circuit Parameter ◽  
Swarm Optimization ◽  
Full Load

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.

scholarly journals A Modified NM-PSO Method for Parameter Estimation Problems of Models

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
An Liu ◽  
Erwie Zahara ◽  
Ming-Ta Yang
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Wide Range ◽  
Estimation Problems

Ordinary differential equations usefully describe the behavior of a wide range of dynamic physical systems. The particle swarm optimization (PSO) method has been considered an effective tool for solving the engineering optimization problems for ordinary differential equations. This paper proposes a modified hybrid Nelder-Mead simplex search and particle swarm optimization (M-NM-PSO) method for solving parameter estimation problems. The M-NM-PSO method improves the efficiency of the PSO method and the conventional NM-PSO method by rapid convergence and better objective function value. Studies are made for three well-known cases, and the solutions of the M-NM-PSO method are compared with those by other methods published in the literature. The results demonstrate that the proposed M-NM-PSO method yields better estimation results than those obtained by the genetic algorithm, the modified genetic algorithm (real-coded GA (RCGA)), the conventional particle swarm optimization (PSO) method, and the conventional NM-PSO method.

scholarly journals Nonintrusive Method for Induction Motor Equivalent Circuit Parameter Estimation using Chicken Swarm Optimization (CSO) Algorithm

2021 ◽  
Vol 18 (1) ◽  
pp. 21-29
Author(s):  
M. Aminu ◽  
M. Abana ◽  
S.W. Pallam ◽  
P.K. Ainah
Keyword(s):  
Parameter Estimation ◽  
Equivalent Circuit ◽  
Induction Motor ◽  
Estimation Method ◽  
Equivalent Circuit Model ◽  
Test Method ◽  
Equivalent Circuits ◽  
Circuit Parameter ◽  
Swarm Optimization ◽  
Chicken Swarm Optimization

This paper presents a nonintrusive method for estimating the parameters of an Induction Motor (IM) without the need for the conventional no-load and locked rotor tests. The method is based on a relatively new swarm-based algorithm called the Chicken Swarm Optimization (CSO). Two different equivalent circuits implementations have been considered for the parameter estimation scheme (one with parallel and the other with series magnetization circuit). The proposed parameter estimation method was validated experimentally on a standard 7.5 kW induction motor and the results were compared to those obtained using the IEEE Std. 112 reduced voltage impedance test method 3. The proposed CSO optimization method gave accurate estimates of the IM equivalent circuit parameters with maximum absolute errors of 5.4618% and 0.9285% for the parallel and series equivalent circuits representations respectively when compared to the IEEE Std. 112 results. However, standard deviation results in terms of the magnetization branch parameters, suggest that the series equivalent circuit model gives more repeatable results when compared to the parallel equivalent circuit. Keywords: Induction motor, Chicken Swarm Optimization, parameter estimation, equivalent circuit, objective function

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