Neural network-based modeling and parameter identification of switched reluctance motors

Wenzhe Lu, Ali Keyhani, Abbas Fardoun

Research output: Contribution to journalArticlepeer-review

69 Citations (Scopus)


Phase windings of switched reluctance machines are modeled by a nonlinear inductance and a resistance that can be estimated from standstill test data. During online operation, the model structures and parameters of SRMs may differ from the standstill ones because of saturation and losses, especially at high current. To model this effect, a damper winding is added into the model structure. This paper proposes an application of artificial neural network to identify the nonlinear model of SRMs from operating data. A two-layer recurrent neural network has been adopted here to estimate the damper currents from phase voltage, phase current, rotor position, and rotor speed. Then, the damper parameters can be identified using maximum likelihood estimation techniques. Finally, the new model and parameters are validated from operating data.

Original languageEnglish
Pages (from-to)284-290
Number of pages7
JournalIEEE Transactions on Energy Conversion
Issue number2
Publication statusPublished - Jun 2003
Externally publishedYes


  • Modeling
  • Neural network
  • Parameter identification
  • Switched reluctance motor

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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