Transient Modelling of Induction Machine Using Artificial Neural Network Surrogate Models

Mikko Tahkola; Victor Mukherjee; Janne Keränen

doi:10.1109/TMAG.2022.3180176

Transient Modelling of Induction Machine Using Artificial Neural Network Surrogate Models

Mikko Tahkola (Corresponding Author), Victor Mukherjee, Janne Keränen

Research output: Contribution to journal › Article › Scientific › peer-review

Abstract

A transient model of an induction machine is developed in this work using an artificial neural network surrogate model. The model is suitable to be used for direct-on-line induction machines. Finite element based model of induction machine is used to generate the training, validation, and testing datasets. Different inputs and model configurations are investigated to find an optimal solution in developing the transient model. The proposed transient model is suitable to be used in digital twin services, since it can estimate the current and torque accurately in real time based on only voltage and measured shaft speed.

Original language	English
Number of pages	4
Journal	IEEE Transactions on Magnetics
DOIs	https://doi.org/10.1109/TMAG.2022.3180176
Publication status	E-pub ahead of print - 3 Jun 2022
MoE publication type	A1 Journal article-refereed

Keywords

artificial neural network , Digital twin , Induction machine , Real time , Surrogate modelling
digital twin
induction machine
real time
surrogate modelling

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10.1109/TMAG.2022.3180176Licence: CC BY

1 Active

Arrowhead Tools: Arrowhead Tools for Engineering of Digitalisation Solutions
Halme, J., Keränen, J. S., Tahkola, M., Pippuri-Mäkeläinen, J. & Farzam Far, M.
1/05/19 → 31/07/22
Project: EU project

Cite this

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title = "Transient Modelling of Induction Machine Using Artificial Neural Network Surrogate Models",

abstract = "A transient model of an induction machine is developed in this work using an artificial neural network surrogate model. The model is suitable to be used for direct-on-line induction machines. Finite element based model of induction machine is used to generate the training, validation, and testing datasets. Different inputs and model configurations are investigated to find an optimal solution in developing the transient model. The proposed transient model is suitable to be used in digital twin services, since it can estimate the current and torque accurately in real time based on only voltage and measured shaft speed.",

keywords = "artificial neural network , Digital twin , Induction machine , Real time , Surrogate modelling, digital twin, induction machine, real time, surrogate modelling",

author = "Mikko Tahkola and Victor Mukherjee and Janne Ker{\"a}nen",

note = "This work has been done in the Arrowhead Tools project, funded by the European Commission through the European H2020 Research and Innovation programme, ECSEL Joint Undertaking, and National Funding Authorities from the 18 countries under grant agreement number 826452. ",

year = "2022",

month = jun,

day = "3",

doi = "10.1109/TMAG.2022.3180176",

language = "English",

journal = "IEEE Transactions on Magnetics",

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publisher = "IEEE Institute of Electrical and Electronic Engineers",

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T1 - Transient Modelling of Induction Machine Using Artificial Neural Network Surrogate Models

AU - Tahkola, Mikko

AU - Mukherjee, Victor

AU - Keränen, Janne

N1 - This work has been done in the Arrowhead Tools project, funded by the European Commission through the European H2020 Research and Innovation programme, ECSEL Joint Undertaking, and National Funding Authorities from the 18 countries under grant agreement number 826452.

PY - 2022/6/3

Y1 - 2022/6/3

N2 - A transient model of an induction machine is developed in this work using an artificial neural network surrogate model. The model is suitable to be used for direct-on-line induction machines. Finite element based model of induction machine is used to generate the training, validation, and testing datasets. Different inputs and model configurations are investigated to find an optimal solution in developing the transient model. The proposed transient model is suitable to be used in digital twin services, since it can estimate the current and torque accurately in real time based on only voltage and measured shaft speed.

AB - A transient model of an induction machine is developed in this work using an artificial neural network surrogate model. The model is suitable to be used for direct-on-line induction machines. Finite element based model of induction machine is used to generate the training, validation, and testing datasets. Different inputs and model configurations are investigated to find an optimal solution in developing the transient model. The proposed transient model is suitable to be used in digital twin services, since it can estimate the current and torque accurately in real time based on only voltage and measured shaft speed.

KW - artificial neural network , Digital twin , Induction machine , Real time , Surrogate modelling

KW - digital twin

KW - induction machine

KW - real time

KW - surrogate modelling

U2 - 10.1109/TMAG.2022.3180176

DO - 10.1109/TMAG.2022.3180176

M3 - Article

JO - IEEE Transactions on Magnetics

JF - IEEE Transactions on Magnetics

SN - 0018-9464

ER -

Transient Modelling of Induction Machine Using Artificial Neural Network Surrogate Models

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Keywords

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Arrowhead Tools: Arrowhead Tools for Engineering of Digitalisation Solutions

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