Transient Modeling of Induction Machine Using Artificial Neural Network Surrogate Models

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

Research output: Contribution to journalArticleScientificpeer-review


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
Issue number9
Publication statusPublished - Sept 2022
MoE publication typeA1 Journal article-refereed


  • 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


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