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

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...

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Bibliographic Details
Published in:IEEE transactions on energy conversion 2001-12, Vol.16 (4), p.305-311
Main Authors: Bora Karayaka, H., Keyhani, A., Heydt, G.T., Agrawal, B.L., Selin, D.A.
Format: Article
Language:English
Subjects:
Artificial neural networks
Computer simulation
Disturbances
Electrical resistance measurement
Generators
Learning theory
Mathematical models
Neural networks
On-line systems
Parameter estimation
Rotors
Shape
Shock absorbers
Stators
Synchronous generators
Voltage
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Summary: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.
ISSN:0885-8969
1558-0059
DOI:10.1109/60.969468