Maximum likelihood estimation of synchronous machine parameters from flux decay data

Abstract

A time-domain system identification procedure for estimating the parameters of a 5-kVA salient pole 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 (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 online small-disturbance test. The results are compared with measured data.<>