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

Abstract

This paper presents a step by step identification procedure of armature, field and saturated parameters of a large steam turbine-generator from real time operating data. First, data from a small excitation disturbance is utilized to estimate armature circuit parameters of the machine. Subsequently, for each set of steady state operating data, saturable mutual inductances L/sub ads/ and L/sub aqs/ are estimated. The recursive maximum likelihood estimation technique is employed for identification in these first two stages. An artificial neural network (ANN) based estimator is used to model these saturated inductances based on the generator operating conditions. Finally, using 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 not used in the training of ANN and with large disturbance responses.