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

H. Bora Karayaka, A. Keyhani, G.T. Heydt, B.L. Agrawal, D.A. Selin
Ohio State Univ., Columbus, OH, USA ;

This paper appears in: IEEE Transactions on Energy Conversion
Publication Date: Dec. 2001
On page(s): 305-311
Volume: 16, Issue: 4
ISSN: 0885-8969
Reference Cited: 18
CODEN: ITCNE4
Inspec Accession Number: 7134052

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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