Artificial Neural Network Modeling of a Synchronous Reluctance Motor

Turan Alp Sarikaya; Caner Korel; Lale T. Ergene

doi:10.1109/SIELA54794.2022.9845776

Artificial Neural Network Modeling of a Synchronous Reluctance Motor

Turan Alp Sarikaya, Caner Korel, Lale T. Ergene

Department of Electrical Engineering

Istanbul Technical University

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

1 Citation (Scopus)

Abstract

Artificial neural networks are applied in different application areas, and electrical machine implementation is one of them. The structures, resembling the motor behavior like motor controllers, observers, parameter prediction units, can also be modeled with artificial neural networks. Choosing network structure, layer, and neuron numbers play a critical role to make the model successful. In this study, multilayer feedforward and recurrent neural networks are trained with various layer and neuron numbers to compare their speed and torque tracking performances. The average training time for each combination and optimal window for neuron and layer numbers are also evaluated.

Original language	English
Title of host publication	2022 22nd International Symposium on Electrical Apparatus and Technologies, SIELA 2022 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665411394
DOIs	https://doi.org/10.1109/SIELA54794.2022.9845776
Publication status	Published - 2022
Event	22nd International Symposium on Electrical Apparatus and Technologies, SIELA 2022 - Bourgas, Bulgaria Duration: 1 Jun 2022 → 4 Jun 2022

Publication series

Name	2022 22nd International Symposium on Electrical Apparatus and Technologies, SIELA 2022 - Proceedings

Conference

Conference	22nd International Symposium on Electrical Apparatus and Technologies, SIELA 2022
Country/Territory	Bulgaria
City	Bourgas
Period	1/06/22 → 4/06/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

Artificial Neural Network
Feedforward ANN
Recurrent ANN
Synchronous Motor
Synchronous Reluctance Motor

Access to Document

10.1109/SIELA54794.2022.9845776

Cite this

Sarikaya, T. A., Korel, C., & Ergene, L. T. (2022). Artificial Neural Network Modeling of a Synchronous Reluctance Motor. In 2022 22nd International Symposium on Electrical Apparatus and Technologies, SIELA 2022 - Proceedings (2022 22nd International Symposium on Electrical Apparatus and Technologies, SIELA 2022 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIELA54794.2022.9845776

Sarikaya, Turan Alp ; Korel, Caner ; Ergene, Lale T. / Artificial Neural Network Modeling of a Synchronous Reluctance Motor. 2022 22nd International Symposium on Electrical Apparatus and Technologies, SIELA 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 22nd International Symposium on Electrical Apparatus and Technologies, SIELA 2022 - Proceedings).

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title = "Artificial Neural Network Modeling of a Synchronous Reluctance Motor",

abstract = "Artificial neural networks are applied in different application areas, and electrical machine implementation is one of them. The structures, resembling the motor behavior like motor controllers, observers, parameter prediction units, can also be modeled with artificial neural networks. Choosing network structure, layer, and neuron numbers play a critical role to make the model successful. In this study, multilayer feedforward and recurrent neural networks are trained with various layer and neuron numbers to compare their speed and torque tracking performances. The average training time for each combination and optimal window for neuron and layer numbers are also evaluated.",

keywords = "Artificial Neural Network, Feedforward ANN, Recurrent ANN, Synchronous Motor, Synchronous Reluctance Motor",

author = "Sarikaya, {Turan Alp} and Caner Korel and Ergene, {Lale T.}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 22nd International Symposium on Electrical Apparatus and Technologies, SIELA 2022 ; Conference date: 01-06-2022 Through 04-06-2022",

year = "2022",

doi = "10.1109/SIELA54794.2022.9845776",

language = "English",

series = "2022 22nd International Symposium on Electrical Apparatus and Technologies, SIELA 2022 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Sarikaya, TA, Korel, C & Ergene, LT 2022, Artificial Neural Network Modeling of a Synchronous Reluctance Motor. in 2022 22nd International Symposium on Electrical Apparatus and Technologies, SIELA 2022 - Proceedings. 2022 22nd International Symposium on Electrical Apparatus and Technologies, SIELA 2022 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 22nd International Symposium on Electrical Apparatus and Technologies, SIELA 2022, Bourgas, Bulgaria, 1/06/22. https://doi.org/10.1109/SIELA54794.2022.9845776

Artificial Neural Network Modeling of a Synchronous Reluctance Motor. / Sarikaya, Turan Alp; Korel, Caner; Ergene, Lale T.
2022 22nd International Symposium on Electrical Apparatus and Technologies, SIELA 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 22nd International Symposium on Electrical Apparatus and Technologies, SIELA 2022 - Proceedings).

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

TY - GEN

T1 - Artificial Neural Network Modeling of a Synchronous Reluctance Motor

AU - Sarikaya, Turan Alp

AU - Korel, Caner

AU - Ergene, Lale T.

PY - 2022

Y1 - 2022

N2 - Artificial neural networks are applied in different application areas, and electrical machine implementation is one of them. The structures, resembling the motor behavior like motor controllers, observers, parameter prediction units, can also be modeled with artificial neural networks. Choosing network structure, layer, and neuron numbers play a critical role to make the model successful. In this study, multilayer feedforward and recurrent neural networks are trained with various layer and neuron numbers to compare their speed and torque tracking performances. The average training time for each combination and optimal window for neuron and layer numbers are also evaluated.

AB - Artificial neural networks are applied in different application areas, and electrical machine implementation is one of them. The structures, resembling the motor behavior like motor controllers, observers, parameter prediction units, can also be modeled with artificial neural networks. Choosing network structure, layer, and neuron numbers play a critical role to make the model successful. In this study, multilayer feedforward and recurrent neural networks are trained with various layer and neuron numbers to compare their speed and torque tracking performances. The average training time for each combination and optimal window for neuron and layer numbers are also evaluated.

KW - Artificial Neural Network

KW - Feedforward ANN

KW - Recurrent ANN

KW - Synchronous Motor

KW - Synchronous Reluctance Motor

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DO - 10.1109/SIELA54794.2022.9845776

M3 - Conference contribution

AN - SCOPUS:85137566208

T3 - 2022 22nd International Symposium on Electrical Apparatus and Technologies, SIELA 2022 - Proceedings

BT - 2022 22nd International Symposium on Electrical Apparatus and Technologies, SIELA 2022 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 22nd International Symposium on Electrical Apparatus and Technologies, SIELA 2022

Y2 - 1 June 2022 through 4 June 2022

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Sarikaya TA, Korel C, Ergene LT. Artificial Neural Network Modeling of a Synchronous Reluctance Motor. In 2022 22nd International Symposium on Electrical Apparatus and Technologies, SIELA 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. (2022 22nd International Symposium on Electrical Apparatus and Technologies, SIELA 2022 - Proceedings). doi: 10.1109/SIELA54794.2022.9845776

Artificial Neural Network Modeling of a Synchronous Reluctance Motor

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Keywords

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