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Title: Maximum likelihood estimation of generator stability constants using SSFR test data

Abstract

This paper presents an evaluation of the performance of the maximum likelihood (ML) method when used to determine simulation data for generators from standstill frequency response (SSFR) tests. The generator or synchronous machine data found by this process or similar processes are used in simulation models for many kinds of stability and dynamic performance calculations. 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 Maximum Likelihood estimation (ML) technique is used to estimate machine parameters.

Authors:
;  [1];  [2]
  1. (Ohio State Univ., Columbus, OH (USA). Dept. of Electrical Engineering)
  2. (American Electric Power Service Corp., Columbus, OH (US))
Publication Date:
OSTI Identifier:
5794300
Resource Type:
Journal Article
Resource Relation:
Journal Name: IEEE Transactions on Energy Conversion (Institute of Electrical and Electronics Engineers); (USA); Journal Volume: 6:1
Country of Publication:
United States
Language:
English
Subject:
20 FOSSIL-FUELED POWER PLANTS; ELECTRIC GENERATORS; STABILITY; COMPUTERIZED SIMULATION; DYNAMICS; MECHANICS; SIMULATION; 200104* - Fossil-Fueled Power Plants- Components

Citation Formats

Keyhani, A., Hao, S., and Schulz, R.P.. Maximum likelihood estimation of generator stability constants using SSFR test data. United States: N. p., 1991. Web. doi:10.1109/60.73801.
Keyhani, A., Hao, S., & Schulz, R.P.. Maximum likelihood estimation of generator stability constants using SSFR test data. United States. doi:10.1109/60.73801.
Keyhani, A., Hao, S., and Schulz, R.P.. Fri . "Maximum likelihood estimation of generator stability constants using SSFR test data". United States. doi:10.1109/60.73801.
@article{osti_5794300,
title = {Maximum likelihood estimation of generator stability constants using SSFR test data},
author = {Keyhani, A. and Hao, S. and Schulz, R.P.},
abstractNote = {This paper presents an evaluation of the performance of the maximum likelihood (ML) method when used to determine simulation data for generators from standstill frequency response (SSFR) tests. The generator or synchronous machine data found by this process or similar processes are used in simulation models for many kinds of stability and dynamic performance calculations. 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 Maximum Likelihood estimation (ML) technique is used to estimate machine parameters.},
doi = {10.1109/60.73801},
journal = {IEEE Transactions on Energy Conversion (Institute of Electrical and Electronics Engineers); (USA)},
number = ,
volume = 6:1,
place = {United States},
year = {Fri Mar 01 00:00:00 EST 1991},
month = {Fri Mar 01 00:00:00 EST 1991}
}
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