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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. 

The structure of power flows in transmission grids is evolving and is likelyto change significantly in the coming years due to the rapid growth ofrenewable energy generation that introduces randomness and bidirectionalpower flows. Another transformative aspect is the increasing penetrationof various smart-meter technologies. Inexpensive measurement devicescan be placed at practically any component of the grid. Using modeldata reflecting smart-meter measurements,we propose a two-stage procedure for detecting a fault in a regional powergrid. In the first stage, a fault is detected in real time. In the second stage,the faulted line is identified with a negligible delay. The approach uses onlythe voltage modulus measured at buses (nodes of the grid) as the input.Our method does not require prior knowledge of thefault type. The method is fully implemented in  R.Pseudo code and complete mathematical formulas are provided. 

This paper explores the use of changepoint detection (CPD) for an improved time-localization of forced oscillations (FOs) in measured power system data. In order for the autoregressive moving average plus sinusoids (ARMA+S) class of electromechanical mode meters to successfully estimate modal frequency and damping from data that contains a FO, accurate estimates of where the FO exists in time series are needed. Compared to the existing correlation-based method, the proposed CPD method is based on upon a maximum likelihood estimator (MLE) for the detection of an unknown number changes in signal mean to unknown levels at unknown times. Using the pruned exact linear time (PELT) dynamic programming algorithm along with a novel refinement technique, the proposed approach is shown to provide a dramatic improvement in FO start/stop time estimation accuracy while being robust to intermittent FOs. These findings were supported though simulations with the minniWECC model.