Maximum likelihood estimation of generator stability constants using SSER test data

The performance of the maximum likelihood (ML) method when used to determine simulation data for generators from standstill frequency response (SSFR) tests is evaluated. The robustness of the ML method is demonstrated by analyses made with SSFR data from tests on the Rockport 722 MVA generator. It is shown that a unique set of parameters can be obtained, and the noise effects can be dealt with effectively when the ML technique is used to estimate machine parameters.<
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