Algorithms for automatic torus motor parameters identification

Comparative study

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Purpose - The parameters of axial-field machines are very small compared with the parameters of conventional machines. Different measuring methods are normally used in order to obtain good estimates of the machine parameters. These methods are difficult to perform, costly and time consuming. This paper proposes the use of genetic algorithms to predict the self and mutual inductances of a specific type of axial-field machine, the Torus motor. Design/methodology/approach - The parameter extraction is reformulated as a search and optimization problem in which the only requirement is a set of values of current versus time and an approximate estimate of the parameters. Findings - The predicted machine self and mutual inductances are verified by comparing with several measuring methods and excellent agreement is obtained. Originality/value - Demonstrates that genetic algorithms can predict the self and mutual inductances of the Torus machine automatically with high accuracy.

Original languageEnglish
Pages (from-to)1299-1310
Number of pages12
JournalCOMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering
Volume24
Issue number4
DOIs
Publication statusPublished - 2005

Fingerprint

Parameter Identification
Inductance
Comparative Study
Identification (control systems)
Torus
Genetic algorithms
Parameter extraction
Genetic Algorithm
Predict
Search Problems
Estimate
Design Methodology
High Accuracy
Optimization Problem
Requirements
Demonstrate

Keywords

  • Inductance
  • Magnetic devices
  • Programming and algorithm theory

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Applied Mathematics

Cite this

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title = "Algorithms for automatic torus motor parameters identification: Comparative study",
abstract = "Purpose - The parameters of axial-field machines are very small compared with the parameters of conventional machines. Different measuring methods are normally used in order to obtain good estimates of the machine parameters. These methods are difficult to perform, costly and time consuming. This paper proposes the use of genetic algorithms to predict the self and mutual inductances of a specific type of axial-field machine, the Torus motor. Design/methodology/approach - The parameter extraction is reformulated as a search and optimization problem in which the only requirement is a set of values of current versus time and an approximate estimate of the parameters. Findings - The predicted machine self and mutual inductances are verified by comparing with several measuring methods and excellent agreement is obtained. Originality/value - Demonstrates that genetic algorithms can predict the self and mutual inductances of the Torus machine automatically with high accuracy.",
keywords = "Inductance, Magnetic devices, Programming and algorithm theory",
author = "Abdullah Al-Badi and Adel Gastli and Jervase, {Joseph A.}",
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T1 - Algorithms for automatic torus motor parameters identification

T2 - Comparative study

AU - Al-Badi, Abdullah

AU - Gastli, Adel

AU - Jervase, Joseph A.

PY - 2005

Y1 - 2005

N2 - Purpose - The parameters of axial-field machines are very small compared with the parameters of conventional machines. Different measuring methods are normally used in order to obtain good estimates of the machine parameters. These methods are difficult to perform, costly and time consuming. This paper proposes the use of genetic algorithms to predict the self and mutual inductances of a specific type of axial-field machine, the Torus motor. Design/methodology/approach - The parameter extraction is reformulated as a search and optimization problem in which the only requirement is a set of values of current versus time and an approximate estimate of the parameters. Findings - The predicted machine self and mutual inductances are verified by comparing with several measuring methods and excellent agreement is obtained. Originality/value - Demonstrates that genetic algorithms can predict the self and mutual inductances of the Torus machine automatically with high accuracy.

AB - Purpose - The parameters of axial-field machines are very small compared with the parameters of conventional machines. Different measuring methods are normally used in order to obtain good estimates of the machine parameters. These methods are difficult to perform, costly and time consuming. This paper proposes the use of genetic algorithms to predict the self and mutual inductances of a specific type of axial-field machine, the Torus motor. Design/methodology/approach - The parameter extraction is reformulated as a search and optimization problem in which the only requirement is a set of values of current versus time and an approximate estimate of the parameters. Findings - The predicted machine self and mutual inductances are verified by comparing with several measuring methods and excellent agreement is obtained. Originality/value - Demonstrates that genetic algorithms can predict the self and mutual inductances of the Torus machine automatically with high accuracy.

KW - Inductance

KW - Magnetic devices

KW - Programming and algorithm theory

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