Title :
Maximum likelihood estimation of generator stability constants using SSER test data
Author :
Keyhani, Ali ; Hao, Shangyou ; Schulz, Richard P.
Author_Institution :
Dept. of Electr. Eng., Ohio State Univ., Columbus, OH, USA
fDate :
3/1/1991 12:00:00 AM
Abstract :
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
Keywords :
electric generators; frequency response; stability; 722 MVA; ML technique; Rockport generator; SSER test data; maximum likelihood estimation; stability constants; standstill frequency response; Maximum likelihood estimation; Noise generators; Nonlinear equations; Parameter estimation; Samarium; Solid modeling; Stability; Synchronous machines; Testing; Time domain analysis;
Journal_Title :
Energy Conversion, IEEE Transactions on
