Identification of armature, field, and saturated parameters of a large steam turbine-generator from operating data

This paper presents a step by step identification procedure of armature, fi eld and saturated parameters of a large steam turbine-generator from real t ime operating data. First, data from a small excitation disturbance is util ized to estimate armature circuit parameters of the machine. Subsequently, for each set of steady state operating data, saturable mutual inductances L -ads and L-aqs are estimated. The recursive maximum likelihood estimation t echnique is employed for identification in these first two stages. An artif icial neural network (ANN) based estimator is later used to model these sat urated inductances based on the generator operating conditions. Finally, us ing the estimates of the armature circuit parameters, the field winding and some damper winding parameters are estimated using an Output Error Method (OEM) of estimation. The developed models are validated with measurements n ot used in the training of ANN and with large disturbance responses.