Transient Modeling of Induction Machine Using Artificial Neural Network Surrogate Models

Mikko Tahkola*, Victor Mukherjee, Janne Keränen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

7 Citations (Scopus)

Abstract

A transient model of an induction machine (IM) is developed in this work using an artificial neural network (ANN) surrogate model. The model is suitable to be used for direct-on-line IMs. The finite-element (FE)-based model of IM 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 languageEnglish
Article number7402204
Number of pages4
JournalIEEE Transactions on Magnetics
Volume58
Issue number9
DOIs
Publication statusPublished - 1 Sept 2022
MoE publication typeA1 Journal article-refereed

Funding

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.

Keywords

  • artificial neural network , Digital twin , Induction machine , Real time , Surrogate modelling
  • digital twin
  • induction machine
  • real time
  • surrogate modelling
  • Torque
  • Predictive models
  • Real time
  • Optimization
  • Shafts
  • Training
  • Digital twin
  • Surrogate modelling
  • Induction machine
  • Artificial neural network
  • Transient analysis
  • Testing
  • surrogate modeling
  • Artificial neural network (ANN)
  • induction machine (IM)

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