Neural network based modeling of a large steam turbine-generator rotor body parameters from on-line disturbance data

H. Bora Karayaka, Ali Keyhani, Gerald Thomas Heydt, Baj L. Agrawal, Douglas A. Selin

Research output: Contribution to journalArticle

15 Scopus citations

Abstract

A novel technique to estimate and model rotor-body parameters of a large steam turbine-generator from real time disturbance data is presented. For each set of disturbance data collected at different operating conditions, the rotor body parameters of the generator are estimated using an Output Error Method (OEM). Artificial neural network (ANN) based estimators are later used to model the nonlinearities in the estimated parameters based on the generator operating conditions. The developed ANN models are then validated with measurements not used in the training procedure. The performance of estimated parameters is also validated with extensive simulations and compared against the manufacturer values.

Original languageEnglish (US)
Pages (from-to)305-311
Number of pages7
JournalIEEE Transactions on Energy Conversion
Volume16
Issue number4
DOIs
StatePublished - Dec 1 2001

Keywords

  • Artificial neural networks
  • Large utility generators
  • Parameter identification
  • Rotor body parameters

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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