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Title: An Iterative Algorithm for Accurate Estimation of Power System Forced Oscillation Parameters
This paper proposes an iterative method of estimating power system forced oscillation (FO) amplitude, frequency, phase, and start/stop times from measured data. It combines three algorithms with favorable asymptotic statistical properties: a periodogram-based iterative frequency estimator, a Discrete-Time Fourier Transform (DTFT)-based method of estimating amplitude and phase, and a changepoint detection (CPD) method for estimating the FO start and stop samples. Each of these have been shown in the literature to be approximate maximum likelihood estimators (MLE), meaning that for large enough sample size or signal-to-noise ratio (SNR), they can be unbiased and reach the Cramer-Rao Lower Bound in variance. The proposed method is shown through Monte Carlo simulations of a low-order model of the Western Electricity Coordinating Council (WECC) power system to achieve statistical efficiency for low SNR values. The proposed method is validated with data measured from the January 11, 2019 US Eastern Interconnection (EI) FO event. It is shown to accurately extract the FO parameters and remove electromechanical mode meter bias, even with a time-varying FO amplitude.  more » « less
Award ID(s):
1944689
PAR ID:
10488490
Author(s) / Creator(s):
;
Publisher / Repository:
IEEE
Date Published:
Journal Name:
2023 IEEE Power & Energy Society General Meeting (PESGM)
ISSN:
1944-9933
ISBN:
978-1-6654-6441-3
Page Range / eLocation ID:
1 to 5
Subject(s) / Keyword(s):
Meters Maximum likelihood estimation Power measurement Monte Carlo methods Force measurement Frequency estimation Power systems
Format(s):
Medium: X
Location:
Orlando, FL, USA
Sponsoring Org:
National Science Foundation
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