Nonlinear estimation of synchronous machine parameters using operating data

Gustavo Valverde, Elias Kyriakides, Gerald T. Heydt, Vladimir Terzija

Research output: Contribution to journalArticlepeer-review

62 Scopus citations


This paper presents a nonlinear parameter estimator for synchronous machines based on the unscented Kalman filter. The proposed methodology uses voltages and current signals recorded from the stator and the field winding to update the parameters of the classical model of the synchronous machine for stability studies. The methodology can be applied without interrupting the normal operation of the generator. Parks Transformation is included in the estimation process to relate the stator measurements (in abc components) to the nonlinear voltage equations in the qd0 reference frame. The proposed robust methodology has been validated using real and simulated data to estimate the model parameters of a 483-MVA round rotor machine.

Original languageEnglish (US)
Article number5937045
Pages (from-to)831-839
Number of pages9
JournalIEEE Transactions on Energy Conversion
Issue number3
StatePublished - Sep 2011


  • Generator modeling
  • parameter estimation
  • synchronous machines
  • unscented Kalman filter (UKF)

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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