Transient Modelling of Induction Machine Using Artificial Neural Network Surrogate Models

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

Research output: Contribution to journalArticleScientificpeer-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 languageEnglish
Number of pages4
JournalIEEE Transactions on Magnetics
DOIs
Publication statusE-pub ahead of print - 3 Jun 2022
MoE publication typeA1 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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