Comparison of several improved versions of particle swarm optimizer algorithm for parameter estimation of squirrel-cage induction motors / Mohammad Yazdani-Asrami, Mehran Taghipour Gorjikolaie and S. Asghar Gholamian

In this paper, three versions of Particle Swarm Optimization (PSO) are proposed to estimate the equivalent circuit parameters of squirrel cage induction motor. It is believed that how inertia weight changes during iterations can impact on final results. Constricted coefficients, linear model and ex...

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Main Authors: Gorjikolaie, Mehran Taghipour, Gholamian, S. Asghar
Format: Article
Language:English
Published: UiTM Perlis 2014
Subjects:
Online Access:https://ir.uitm.edu.my/id/eprint/12394/
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author Gorjikolaie, Mehran Taghipour
Gholamian, S. Asghar
author_facet Gorjikolaie, Mehran Taghipour
Gholamian, S. Asghar
author_sort Gorjikolaie, Mehran Taghipour
building UiTM Institutional Repository
collection Online Access
description In this paper, three versions of Particle Swarm Optimization (PSO) are proposed to estimate the equivalent circuit parameters of squirrel cage induction motor. It is believed that how inertia weight changes during iterations can impact on final results. Constricted coefficients, linear model and exponential version are used as inertia weight, each of them presents different variations for inertia weight and consequently for particle movements and speed of such movements. In the linear version, particles start searching process with high speed and their speed will decrease by constant ramp, this kind of variation let to search all solution space in a short time and local search at the final iterations with low speed, also exponential version presents same treatment as linear version with non-linear variations in inertia weight and speed of movement. But, mathematical analysis shows that they trap into local minima and scientists presents constricted version to solve this problem. In order to evaluate proposed versions additional to make changing in PSO’s version, sensitivity of proposed methods is analyzed using three sets of data. Results confirm the ability of proposed method which can estimate parameters with a possible least error.
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spelling uitm-123942019-03-12T03:41:21Z https://ir.uitm.edu.my/id/eprint/12394/ Comparison of several improved versions of particle swarm optimizer algorithm for parameter estimation of squirrel-cage induction motors / Mohammad Yazdani-Asrami, Mehran Taghipour Gorjikolaie and S. Asghar Gholamian jurnalintelek Gorjikolaie, Mehran Taghipour Gholamian, S. Asghar TJ Mechanical engineering and machinery In this paper, three versions of Particle Swarm Optimization (PSO) are proposed to estimate the equivalent circuit parameters of squirrel cage induction motor. It is believed that how inertia weight changes during iterations can impact on final results. Constricted coefficients, linear model and exponential version are used as inertia weight, each of them presents different variations for inertia weight and consequently for particle movements and speed of such movements. In the linear version, particles start searching process with high speed and their speed will decrease by constant ramp, this kind of variation let to search all solution space in a short time and local search at the final iterations with low speed, also exponential version presents same treatment as linear version with non-linear variations in inertia weight and speed of movement. But, mathematical analysis shows that they trap into local minima and scientists presents constricted version to solve this problem. In order to evaluate proposed versions additional to make changing in PSO’s version, sensitivity of proposed methods is analyzed using three sets of data. Results confirm the ability of proposed method which can estimate parameters with a possible least error. UiTM Perlis 2014 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/12394/2/AJ_MOHAMMAD%20YAZDANI-ASRAMI%20JI%2014.pdf Gorjikolaie, Mehran Taghipour and Gholamian, S. Asghar (2014) Comparison of several improved versions of particle swarm optimizer algorithm for parameter estimation of squirrel-cage induction motors / Mohammad Yazdani-Asrami, Mehran Taghipour Gorjikolaie and S. Asghar Gholamian. (2014) Jurnal Intelek <https://ir.uitm.edu.my/view/publication/Jurnal_Intelek.html>, 8 (2). pp. 27-35. ISSN 2231-7716
spellingShingle TJ Mechanical engineering and machinery
Gorjikolaie, Mehran Taghipour
Gholamian, S. Asghar
Comparison of several improved versions of particle swarm optimizer algorithm for parameter estimation of squirrel-cage induction motors / Mohammad Yazdani-Asrami, Mehran Taghipour Gorjikolaie and S. Asghar Gholamian
title Comparison of several improved versions of particle swarm optimizer algorithm for parameter estimation of squirrel-cage induction motors / Mohammad Yazdani-Asrami, Mehran Taghipour Gorjikolaie and S. Asghar Gholamian
title_full Comparison of several improved versions of particle swarm optimizer algorithm for parameter estimation of squirrel-cage induction motors / Mohammad Yazdani-Asrami, Mehran Taghipour Gorjikolaie and S. Asghar Gholamian
title_fullStr Comparison of several improved versions of particle swarm optimizer algorithm for parameter estimation of squirrel-cage induction motors / Mohammad Yazdani-Asrami, Mehran Taghipour Gorjikolaie and S. Asghar Gholamian
title_full_unstemmed Comparison of several improved versions of particle swarm optimizer algorithm for parameter estimation of squirrel-cage induction motors / Mohammad Yazdani-Asrami, Mehran Taghipour Gorjikolaie and S. Asghar Gholamian
title_short Comparison of several improved versions of particle swarm optimizer algorithm for parameter estimation of squirrel-cage induction motors / Mohammad Yazdani-Asrami, Mehran Taghipour Gorjikolaie and S. Asghar Gholamian
title_sort comparison of several improved versions of particle swarm optimizer algorithm for parameter estimation of squirrel-cage induction motors / mohammad yazdani-asrami, mehran taghipour gorjikolaie and s. asghar gholamian
topic TJ Mechanical engineering and machinery
url https://ir.uitm.edu.my/id/eprint/12394/