This paper presents a step by step identification procedure of armature, field and saturated parameters of a large utility generator from real time operating data. First, a small excitation disturbance data is utilized to estimate armature circuit parameters of the machine. Subsequently, for each steady state operating data, saturable mutual inductances L_(ads) and L_(ags) 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 later, used to model these saturated inductances based on the generator operating conditions. Finally, based on the estimates of the armature circuit parameters, the field winding and some damper winding parameters are estimated using an Output Error Method (OEM) technique. The developed models are validated with measurements not used in the training of ANN and with large disturbance responses.
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