Maximum likelihood estimation of synchronous machine parameters from flux decay data
A. Tumageanian, A. Keyhani, S.I. Moon, T. Leksan, L. Xu
Ohio State Univ, Columbus, OH, USA ;
This paper appears in : IEEE Transactions on Industry Applications
Date: March/April 1994
Abstract:
A time-domain system identification procedure to
estimate the parameters of a 5 kVA salient pole synchronous
machine from standstill test measurements is proposed. The test
consists of a dc flux decay signal applied to the d-axis and q-axis
of the machine. From the recorded responses to this signal,
the admittance transfer function models and the SSFR equivalent
circuit models are identified. The Maximum Likelihood algorithm
is used to estimate the model parameter values, and the Akaike
Criterion is used to select the best-fit model. The performance
of the standstill models in the dynamic environment is studied
through simulation of an on-line small-disturbance test. The
results are compared with measured data.