IDENTIFICATION OF HIGH-ORDER SYNCHRONOUS GENERATOR MODELS FROM SSFR TEST DATA

This paper presents a direct maximum-likelihood (ML) estimation procedure to identify the synchronous machine models based on the standstill frequency response (SSFR) test data. The method presented in this study is the first and only algorithm utilizing all available SSFR test data under both shorted and open field circuit conditions to establish a unique equivalent circuit model by maximizing the conditional probability density function of the error residuals. The method is applied for modeling of two well-known generators, namely the Rockport and Nanticoke generators [1,3], using the measured SSFR test data. The results of the study show that by incorporating both the open and short-circuit SSFR data in the modeling process, the SSFR characteristics of the two generators can be accurately represented by the established high order synchronous models up to 1 kHz. The identified synchronous machine model consists of five amortiseur windings on each axis. In addition, an eddy-current effect impedance is included in the d-axis model for representing the increased influence of rotor eddy current under the open-circuit test condition.