Maximum likelihood estimation of synchronous machine parameters from flux decay data

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 standstill frequency response 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