Artificial intelligence based torque ripple minimization of Switched Reluctance Motor drives

Eid Gouda; M. Hamouda; A. R.A. Amin

doi:10.1109/MEPCON.2016.7837010

Artificial intelligence based torque ripple minimization of Switched Reluctance Motor drives

Eid Gouda^*, M. Hamouda, A. R.A. Amin

^*Corresponding author for this work

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

22 Scopus citations

Abstract

The Switched Reluctance Motors (SRMs) are designed for many applications. They have wound field coils for the stator windings. Their rotor however has no magnets or coils attached. Their main drawback is the torque ripple. The torque ripple minimization is reduced in this paper using Torque Sharing Function (TSF) technique for four quadrants operation of the motor. The Artificial Neural Network (ANN) is used to translate the torque reference to suitable current. With the last advance of microcontrollers and DSPs, the implementation of the ANN becomes less complex and inexpensive. This simplifies the controller and cuts down its cost. For accurate modelling and high effective results, the authors use the Finite Element Analysis (FEA) for obtaining the magnetization characteristics of the motor. The SRM is modelled and simulated based on Matlab/Simulink. The torque ripples obtained by the proposed model are kept minor for the various operation modes of the motor.

Original language	English
Title of host publication	2016 18th International Middle-East Power Systems Conference, MEPCON 2016 - Proceedings
Editors	Omar H. Abdalla
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	943-948
Number of pages	6
ISBN (Electronic)	9781467390637
DOIs	https://doi.org/10.1109/MEPCON.2016.7837010
State	Published - 30 Jan 2017
Externally published	Yes

Publication series

Name	2016 18th International Middle-East Power Systems Conference, MEPCON 2016 - Proceedings

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

ANN
FEM
SRM
Simulink
TRM
TSF

ASJC Scopus subject areas

Renewable Energy, Sustainability and the Environment
Energy Engineering and Power Technology
Control and Optimization

Access to Document

10.1109/MEPCON.2016.7837010

Cite this

Gouda, E., Hamouda, M., & Amin, A. R. A. (2017). Artificial intelligence based torque ripple minimization of Switched Reluctance Motor drives. In O. H. Abdalla (Ed.), 2016 18th International Middle-East Power Systems Conference, MEPCON 2016 - Proceedings (pp. 943-948). Article 7837010 (2016 18th International Middle-East Power Systems Conference, MEPCON 2016 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MEPCON.2016.7837010

Gouda, Eid ; Hamouda, M. ; Amin, A. R.A. / Artificial intelligence based torque ripple minimization of Switched Reluctance Motor drives. 2016 18th International Middle-East Power Systems Conference, MEPCON 2016 - Proceedings. editor / Omar H. Abdalla. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 943-948 (2016 18th International Middle-East Power Systems Conference, MEPCON 2016 - Proceedings).

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title = "Artificial intelligence based torque ripple minimization of Switched Reluctance Motor drives",

abstract = "The Switched Reluctance Motors (SRMs) are designed for many applications. They have wound field coils for the stator windings. Their rotor however has no magnets or coils attached. Their main drawback is the torque ripple. The torque ripple minimization is reduced in this paper using Torque Sharing Function (TSF) technique for four quadrants operation of the motor. The Artificial Neural Network (ANN) is used to translate the torque reference to suitable current. With the last advance of microcontrollers and DSPs, the implementation of the ANN becomes less complex and inexpensive. This simplifies the controller and cuts down its cost. For accurate modelling and high effective results, the authors use the Finite Element Analysis (FEA) for obtaining the magnetization characteristics of the motor. The SRM is modelled and simulated based on Matlab/Simulink. The torque ripples obtained by the proposed model are kept minor for the various operation modes of the motor.",

keywords = "ANN, FEM, SRM, Simulink, TRM, TSF",

author = "Eid Gouda and M. Hamouda and Amin, \{A. R.A.\}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.",

year = "2017",

month = jan,

day = "30",

doi = "10.1109/MEPCON.2016.7837010",

language = "English",

series = "2016 18th International Middle-East Power Systems Conference, MEPCON 2016 - Proceedings",

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Gouda, E, Hamouda, M & Amin, ARA 2017, Artificial intelligence based torque ripple minimization of Switched Reluctance Motor drives. in OH Abdalla (ed.), 2016 18th International Middle-East Power Systems Conference, MEPCON 2016 - Proceedings., 7837010, 2016 18th International Middle-East Power Systems Conference, MEPCON 2016 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 943-948. https://doi.org/10.1109/MEPCON.2016.7837010

Artificial intelligence based torque ripple minimization of Switched Reluctance Motor drives. / Gouda, Eid; Hamouda, M.; Amin, A. R.A.
2016 18th International Middle-East Power Systems Conference, MEPCON 2016 - Proceedings. ed. / Omar H. Abdalla. Institute of Electrical and Electronics Engineers Inc., 2017. p. 943-948 7837010 (2016 18th International Middle-East Power Systems Conference, MEPCON 2016 - Proceedings).

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

TY - GEN

T1 - Artificial intelligence based torque ripple minimization of Switched Reluctance Motor drives

AU - Gouda, Eid

AU - Hamouda, M.

AU - Amin, A. R.A.

PY - 2017/1/30

Y1 - 2017/1/30

N2 - The Switched Reluctance Motors (SRMs) are designed for many applications. They have wound field coils for the stator windings. Their rotor however has no magnets or coils attached. Their main drawback is the torque ripple. The torque ripple minimization is reduced in this paper using Torque Sharing Function (TSF) technique for four quadrants operation of the motor. The Artificial Neural Network (ANN) is used to translate the torque reference to suitable current. With the last advance of microcontrollers and DSPs, the implementation of the ANN becomes less complex and inexpensive. This simplifies the controller and cuts down its cost. For accurate modelling and high effective results, the authors use the Finite Element Analysis (FEA) for obtaining the magnetization characteristics of the motor. The SRM is modelled and simulated based on Matlab/Simulink. The torque ripples obtained by the proposed model are kept minor for the various operation modes of the motor.

AB - The Switched Reluctance Motors (SRMs) are designed for many applications. They have wound field coils for the stator windings. Their rotor however has no magnets or coils attached. Their main drawback is the torque ripple. The torque ripple minimization is reduced in this paper using Torque Sharing Function (TSF) technique for four quadrants operation of the motor. The Artificial Neural Network (ANN) is used to translate the torque reference to suitable current. With the last advance of microcontrollers and DSPs, the implementation of the ANN becomes less complex and inexpensive. This simplifies the controller and cuts down its cost. For accurate modelling and high effective results, the authors use the Finite Element Analysis (FEA) for obtaining the magnetization characteristics of the motor. The SRM is modelled and simulated based on Matlab/Simulink. The torque ripples obtained by the proposed model are kept minor for the various operation modes of the motor.

KW - ANN

KW - FEM

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KW - TRM

KW - TSF

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M3 - Conference contribution

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T3 - 2016 18th International Middle-East Power Systems Conference, MEPCON 2016 - Proceedings

SP - 943

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BT - 2016 18th International Middle-East Power Systems Conference, MEPCON 2016 - Proceedings

A2 - Abdalla, Omar H.

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ER -

Gouda E, Hamouda M, Amin ARA. Artificial intelligence based torque ripple minimization of Switched Reluctance Motor drives. In Abdalla OH, editor, 2016 18th International Middle-East Power Systems Conference, MEPCON 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 943-948. 7837010. (2016 18th International Middle-East Power Systems Conference, MEPCON 2016 - Proceedings). doi: 10.1109/MEPCON.2016.7837010

Artificial intelligence based torque ripple minimization of Switched Reluctance Motor drives

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this