On the identifiability of steady-state induction machine models using external measurements

Ahmed M. Alturas, Shady M. Gadoue, Bashar Zahawi, Mohammed A. Elgendy

Research output: Contribution to journalArticle

Abstract

A common practice in induction machine parameter identification techniques is to use external measurements of voltage, current, speed, and/or torque. Using this approach, it has been shown that it is possible to obtain an infinite number of mathematical solutions representing the machine parameters. This paper examines the identifiability of two commonly used induction machine models, namely the T-model (the conventional per phase equivalent circuit) and the inverse Γ-model. A novel approach based on the alternating conditional expectation (ACE) algorithm is employed here for the first time to study the identifiability of the two induction machine models. The results obtained from the proposed ACE algorithm show that the parameters of the commonly employed T-model are unidentifiable, unlike the parameters of the inverse Γ-model which are uniquely identifiable from external measurements. The identifiability analysis results are experimentally verified using the measured operating characteristics of a 1.1-kW three-phase induction machine in conjunction with the Levenberg-Marquardt algorithm, which is developed and applied here for this purpose.

Original languageEnglish
Article number7202861
Pages (from-to)251-259
Number of pages9
JournalIEEE Transactions on Energy Conversion
Volume31
Issue number1
Early online date14 Aug 2015
DOIs
Publication statusPublished - 1 Mar 2016

Fingerprint

Equivalent circuits
Identification (control systems)
Torque
Electric potential

Bibliographical note

© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Keywords

  • identifiability analysis
  • Induction motor
  • parameter identification

Cite this

Alturas, Ahmed M. ; Gadoue, Shady M. ; Zahawi, Bashar ; Elgendy, Mohammed A. / On the identifiability of steady-state induction machine models using external measurements. In: IEEE Transactions on Energy Conversion. 2016 ; Vol. 31, No. 1. pp. 251-259.
@article{26d97915eaba4ab7980f3585713dc31c,
title = "On the identifiability of steady-state induction machine models using external measurements",
abstract = "A common practice in induction machine parameter identification techniques is to use external measurements of voltage, current, speed, and/or torque. Using this approach, it has been shown that it is possible to obtain an infinite number of mathematical solutions representing the machine parameters. This paper examines the identifiability of two commonly used induction machine models, namely the T-model (the conventional per phase equivalent circuit) and the inverse Γ-model. A novel approach based on the alternating conditional expectation (ACE) algorithm is employed here for the first time to study the identifiability of the two induction machine models. The results obtained from the proposed ACE algorithm show that the parameters of the commonly employed T-model are unidentifiable, unlike the parameters of the inverse Γ-model which are uniquely identifiable from external measurements. The identifiability analysis results are experimentally verified using the measured operating characteristics of a 1.1-kW three-phase induction machine in conjunction with the Levenberg-Marquardt algorithm, which is developed and applied here for this purpose.",
keywords = "identifiability analysis, Induction motor, parameter identification",
author = "Alturas, {Ahmed M.} and Gadoue, {Shady M.} and Bashar Zahawi and Elgendy, {Mohammed A.}",
note = "{\circledC} 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.",
year = "2016",
month = "3",
day = "1",
doi = "10.1109/TEC.2015.2460456",
language = "English",
volume = "31",
pages = "251--259",
journal = "IEEE Transactions on Energy Conversion",
issn = "0885-8969",
publisher = "IEEE",
number = "1",

}

On the identifiability of steady-state induction machine models using external measurements. / Alturas, Ahmed M.; Gadoue, Shady M.; Zahawi, Bashar; Elgendy, Mohammed A.

In: IEEE Transactions on Energy Conversion, Vol. 31, No. 1, 7202861, 01.03.2016, p. 251-259.

Research output: Contribution to journalArticle

TY - JOUR

T1 - On the identifiability of steady-state induction machine models using external measurements

AU - Alturas, Ahmed M.

AU - Gadoue, Shady M.

AU - Zahawi, Bashar

AU - Elgendy, Mohammed A.

N1 - © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2016/3/1

Y1 - 2016/3/1

N2 - A common practice in induction machine parameter identification techniques is to use external measurements of voltage, current, speed, and/or torque. Using this approach, it has been shown that it is possible to obtain an infinite number of mathematical solutions representing the machine parameters. This paper examines the identifiability of two commonly used induction machine models, namely the T-model (the conventional per phase equivalent circuit) and the inverse Γ-model. A novel approach based on the alternating conditional expectation (ACE) algorithm is employed here for the first time to study the identifiability of the two induction machine models. The results obtained from the proposed ACE algorithm show that the parameters of the commonly employed T-model are unidentifiable, unlike the parameters of the inverse Γ-model which are uniquely identifiable from external measurements. The identifiability analysis results are experimentally verified using the measured operating characteristics of a 1.1-kW three-phase induction machine in conjunction with the Levenberg-Marquardt algorithm, which is developed and applied here for this purpose.

AB - A common practice in induction machine parameter identification techniques is to use external measurements of voltage, current, speed, and/or torque. Using this approach, it has been shown that it is possible to obtain an infinite number of mathematical solutions representing the machine parameters. This paper examines the identifiability of two commonly used induction machine models, namely the T-model (the conventional per phase equivalent circuit) and the inverse Γ-model. A novel approach based on the alternating conditional expectation (ACE) algorithm is employed here for the first time to study the identifiability of the two induction machine models. The results obtained from the proposed ACE algorithm show that the parameters of the commonly employed T-model are unidentifiable, unlike the parameters of the inverse Γ-model which are uniquely identifiable from external measurements. The identifiability analysis results are experimentally verified using the measured operating characteristics of a 1.1-kW three-phase induction machine in conjunction with the Levenberg-Marquardt algorithm, which is developed and applied here for this purpose.

KW - identifiability analysis

KW - Induction motor

KW - parameter identification

UR - http://www.scopus.com/inward/record.url?scp=84939458771&partnerID=8YFLogxK

UR - https://ieeexplore.ieee.org/document/7202861

U2 - 10.1109/TEC.2015.2460456

DO - 10.1109/TEC.2015.2460456

M3 - Article

AN - SCOPUS:84939458771

VL - 31

SP - 251

EP - 259

JO - IEEE Transactions on Energy Conversion

JF - IEEE Transactions on Energy Conversion

SN - 0885-8969

IS - 1

M1 - 7202861

ER -