Synchronous Machine Parameter Identification

ABSTRACT This paper presents a survey of synchronous machine modeling and parameter identification. In addition, the paper presents results of a study conducted to estimate the effects of measurement noise on the estimation of machine parameters from Standstill Frequency Response (SSFR) test data. Results obtained indicate that because the test data are inherently noise-corrupted, multiple solution sets can be obtained. Furthermore, the effect of noise on time-domain parameter estimation of synchronous machine models axe studied. It is shown that a unique set of parameters can be obtained and the noise effects can be dealt with effectively when the maximum likelihood estimatiou (ML) technique is used to estimate machine parameters.

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