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
Improved Time-Localization of Power System Forced Oscillations Using Changepoint Detection
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.
more »
« less
- Award ID(s):
- 1944689
- NSF-PAR ID:
- 10380847
- Date Published:
- Journal Name:
- 2022 IEEE Power & Energy Society General Meeting (PESGM)
- Page Range / eLocation ID:
- 01 to 05
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Given a sequence of possibly correlated randomly generated graphs, we address the problem of detecting changes on their underlying distribution. To this end, we will consider Random Dot Product Graphs (RDPGs), a simple yet rich family of random graphs that subsume Erdös-Rényi and Stochastic Block Model ensembles as particular cases. In RDPGs each node has an associated latent vector and inner products between these vectors dictate the edge existence probabilities. Previous works have mostly focused on the undirected and unweighted graph case, a gap we aim to close here. We first extend the RDPG model to accommodate directed and weighted graphs, a contribution whose interest transcends change-point detection (CPD). A statistic derived from the nodes' estimated latent vectors (i.e., embeddings) facilitates adoption of scalable geometric CPD techniques. The resulting algorithm yields interpretable results and facilitates pinpointing which (and when) nodes are acting differently. Numerical tests on simulated data as well as on a real dataset of graphs stemming from a Wi-Fi network corroborate the effectiveness of the proposed CPD method.more » « less
-
null (Ed.)Abstract Study Objectives The usage of wrist-worn wearables to detect sleep–wake states remains a formidable challenge, particularly among individuals with disordered sleep. We developed a novel and unbiased data-driven method for the detection of sleep–wake and compared its performance with the well-established Oakley algorithm (OA) relative to polysomnography (PSG) in elderly men with disordered sleep. Methods Overnight in-lab PSG from 102 participants was compared with accelerometry and photoplethysmography simultaneously collected with a wearable device (Empatica E4). A binary segmentation algorithm was used to detect change points in these signals. A model that estimates sleep or wake states given the changes in these signals was established (change point decoder, CPD). The CPD’s performance was compared with the performance of the OA in relation to PSG. Results On the testing set, OA provided sleep accuracy of 0.85, wake accuracy of 0.54, AUC of 0.67, and Kappa of 0.39. Comparable values for CPD were 0.70, 0.74, 0.78, and 0.40. The CPD method had sleep onset latency error of −22.9 min, sleep efficiency error of 2.09%, and underestimated the number of sleep–wake transitions with an error of 64.4. The OA method’s performance was 28.6 min, −0.03%, and −17.2, respectively. Conclusions The CPD aggregates information from both cardiac and motion signals for state determination as well as the cross-dimensional influences from these domains. Therefore, CPD classification achieved balanced performance and higher AUC, despite underestimating sleep–wake transitions. The CPD could be used as an alternate framework to investigate sleep–wake dynamics within the conventional time frame of 30-s epochs.more » « less
-
Given a sequence of random graphs, we address the problem of online monitoring and detection of changes in the underlying data distribution. To this end, we adopt the Random Dot Product Graph (RDPG) model which postulates each node has an associated latent vector, and inner products between these vectors dictate the edge formation probabilities. Existing approaches for graph change-point detection (CPD) rely either on extensive computation, or they store and process the entire observed time series. In this paper we consider the cumulative sum of a judicious monitoring function, which quantifies the discrepancy between the streaming graph observations and the nominal model. This reference distribution is inferred via spectral embeddings of the first few graphs in the sequence, and the monitoring function can be updated in an efficient, online fashion. We characterize the distribution of this running statistic, allowing us to select appropriate thresholding parameters that guarantee error-rate control. The end result is a lightweight online CPD algorithm, with a proven capability to flag distribution shifts in the arriving graphs. The novel method is tested on both synthetic and real network data, corroborating its effectiveness in quickly detecting changes in the input graph sequence.more » « less
-
more » « less
Abstract The peak alignment is a vital preprocessing step before downstream analysis, such as biomarker discovery and pathway analysis, for two‐dimensional gas chromatography mass spectrometry (2DGCMS)–based metabolomics data. Due to uncontrollable experimental conditions, for example, the differences in temperature or pressure, matrix effects on samples, and stationary phase degradation, a shift of retention times among samples inevitably occurs during 2DGCMS experiments, making it difficult to align peaks. Various peak alignment algorithms have been developed to correct retention time shifts for homogeneous, heterogeneous, or both types of mass spectrometry data. However, almost all existing algorithms have been focused on a local alignment and are suffering from low accuracy especially when aligning dense biological data with many peaks. We have developed four global peak alignment (GPA) algorithms using coherent point drift (CPD) point matching algorithms: retention time‐based CPD‐GPA (RT), prior CPD‐GPA (P), mixture CPD‐GPA (M), and prior mixture CPD‐GPA (P + M). Method RT performs the peak alignment based only on the retention time distance, while methods P, M, and P + M carry out the peak alignment using both the retention time distance and mass spectral similarity. Method P incorporates the mass spectral similarity through prior information, and Methods M and P + M use the mixture distance measure. Four developed algorithms are applied to homogeneous and heterogeneous spiked‐in data as well as two real biological data and compared with three existing algorithms, mSPA, SWPA, and BiPACE‐2D. The results show that our CPD‐GPA algorithms perform better than all existing algorithms in terms of F1 score.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/PESGM48719.2022.9916848
