Maximum likelihood estimation of generator stability constants using SSFR test data

Journal Article · Fri Mar 01 04:00:00 EST 1991 · IEEE Transactions on Energy Conversion (Institute of Electrical and Electronics Engineers); (USA)

DOI:https://doi.org/10.1109/60.73801· OSTI ID:5794300

Keyhani, A; Hao, S ^[1]; Schulz, R P ^[2]

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1. Ohio State Univ., Columbus, OH (USA). Dept. of Electrical Engineering
2. American Electric Power Service Corp., Columbus, OH (US)

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.

• OSTI ID: 5794300
• Journal Information: IEEE Transactions on Energy Conversion (Institute of Electrical and Electronics Engineers); (USA), Journal Name: IEEE Transactions on Energy Conversion (Institute of Electrical and Electronics Engineers); (USA) Vol. 6:1; ISSN 0885-8969; ISSN ITCNE
• Country of Publication: United States
• Language: English

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