Artificial neural network based direct torque control of induction motor drives

Rajesh Kumar; R. A. Gupta; S. V. Bhangale; Himanshu Gothwal

doi:10.1049/ic:20070638

Artificial neural network based direct torque control of induction motor drives

Rajesh Kumar
, R. A. Gupta
, S. V. Bhangale
, Himanshu Gothwal

Malaviya National Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

15 Citations (Scopus)

Abstract

Direct Torque Control (DTC) of Induction Motor drive has quick torque response without complex orientation transformation and inner loop current control. Although DTC has some drawbacks, such as the torque and flux ripple. The important point in DTC is the right selection of the stator voltage vector.This paper presents simple structured neural networks for flux position estimation, sector selection and stator voltage vector selection for induction motors using direct torque control (DTC) method. The Levenberg-Marquardt back-propagation technique has been used to train the neural network. The simple structure network facilitates a short training and processing times. The conventional flux position estimator, sector selector and stator voltage vector selector based DTC scheme compared with the proposed scheme and the results are validated through simulation.

Original language	English
Title of host publication	IET-UK International Conference on Information and Communication Technology in Electrical Sciences, ICTES 2007
Pages	361-367
Number of pages	7
Edition	2
DOIs	https://doi.org/10.1049/ic:20070638
Publication status	Published - 2007
Externally published	Yes
Event	IET-UK International Conference on Information and Communication Technology in Electrical Sciences, ICTES 2007 - Tamil Nadu, India Duration: 20 Dec 2007 → 22 Dec 2007

Publication series

Name	IET Seminar Digest
Number	2
Volume	2007

Conference

Conference	IET-UK International Conference on Information and Communication Technology in Electrical Sciences, ICTES 2007
Country/Territory	India
City	Tamil Nadu
Period	20/12/07 → 22/12/07

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

ANN
Direct torque control (DTC)
Flux position estimator
Induction motor
Sector selector

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.1049/ic:20070638

Cite this

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title = "Artificial neural network based direct torque control of induction motor drives",

abstract = "Direct Torque Control (DTC) of Induction Motor drive has quick torque response without complex orientation transformation and inner loop current control. Although DTC has some drawbacks, such as the torque and flux ripple. The important point in DTC is the right selection of the stator voltage vector.This paper presents simple structured neural networks for flux position estimation, sector selection and stator voltage vector selection for induction motors using direct torque control (DTC) method. The Levenberg-Marquardt back-propagation technique has been used to train the neural network. The simple structure network facilitates a short training and processing times. The conventional flux position estimator, sector selector and stator voltage vector selector based DTC scheme compared with the proposed scheme and the results are validated through simulation.",

keywords = "ANN, Direct torque control (DTC), Flux position estimator, Induction motor, Sector selector",

author = "Rajesh Kumar and Gupta, \{R. A.\} and Bhangale, \{S. V.\} and Himanshu Gothwal",

year = "2007",

doi = "10.1049/ic:20070638",

language = "English",

isbn = "9780863419379",

series = "IET Seminar Digest",

number = "2",

pages = "361--367",

booktitle = "IET-UK International Conference on Information and Communication Technology in Electrical Sciences, ICTES 2007",

edition = "2",

note = "IET-UK International Conference on Information and Communication Technology in Electrical Sciences, ICTES 2007 ; Conference date: 20-12-2007 Through 22-12-2007",

}

Kumar, R, Gupta, RA, Bhangale, SV & Gothwal, H 2007, Artificial neural network based direct torque control of induction motor drives. in IET-UK International Conference on Information and Communication Technology in Electrical Sciences, ICTES 2007. 2 edn, IET Seminar Digest, no. 2, vol. 2007, pp. 361-367, IET-UK International Conference on Information and Communication Technology in Electrical Sciences, ICTES 2007, Tamil Nadu, India, 20/12/07. https://doi.org/10.1049/ic:20070638

Artificial neural network based direct torque control of induction motor drives. / Kumar, Rajesh; Gupta, R. A.; Bhangale, S. V. et al.
IET-UK International Conference on Information and Communication Technology in Electrical Sciences, ICTES 2007. 2. ed. 2007. p. 361-367 (IET Seminar Digest; Vol. 2007, No. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Artificial neural network based direct torque control of induction motor drives

AU - Kumar, Rajesh

AU - Gupta, R. A.

AU - Bhangale, S. V.

AU - Gothwal, Himanshu

PY - 2007

Y1 - 2007

N2 - Direct Torque Control (DTC) of Induction Motor drive has quick torque response without complex orientation transformation and inner loop current control. Although DTC has some drawbacks, such as the torque and flux ripple. The important point in DTC is the right selection of the stator voltage vector.This paper presents simple structured neural networks for flux position estimation, sector selection and stator voltage vector selection for induction motors using direct torque control (DTC) method. The Levenberg-Marquardt back-propagation technique has been used to train the neural network. The simple structure network facilitates a short training and processing times. The conventional flux position estimator, sector selector and stator voltage vector selector based DTC scheme compared with the proposed scheme and the results are validated through simulation.

AB - Direct Torque Control (DTC) of Induction Motor drive has quick torque response without complex orientation transformation and inner loop current control. Although DTC has some drawbacks, such as the torque and flux ripple. The important point in DTC is the right selection of the stator voltage vector.This paper presents simple structured neural networks for flux position estimation, sector selection and stator voltage vector selection for induction motors using direct torque control (DTC) method. The Levenberg-Marquardt back-propagation technique has been used to train the neural network. The simple structure network facilitates a short training and processing times. The conventional flux position estimator, sector selector and stator voltage vector selector based DTC scheme compared with the proposed scheme and the results are validated through simulation.

KW - ANN

KW - Direct torque control (DTC)

KW - Flux position estimator

KW - Induction motor

KW - Sector selector

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DO - 10.1049/ic:20070638

M3 - Conference contribution

AN - SCOPUS:67650407665

SN - 9780863419379

T3 - IET Seminar Digest

SP - 361

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BT - IET-UK International Conference on Information and Communication Technology in Electrical Sciences, ICTES 2007

T2 - IET-UK International Conference on Information and Communication Technology in Electrical Sciences, ICTES 2007

Y2 - 20 December 2007 through 22 December 2007

ER -

Artificial neural network based direct torque control of induction motor drives

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this