Power grid frequency prediction using spatiotemporal modeling

Lenzi, Amanda; Bessac, Julie; Anitescu, Mihai

doi:10.1002/sam.11535

Power grid frequency prediction using spatiotemporal modeling

Journal Article · Tue Jul 06 00:00:00 EDT 2021 · Statistical Analysis and Data Mining

DOI:https://doi.org/10.1002/sam.11535· OSTI ID:1863260

^[1]; Bessac, Julie ^[1]; Anitescu, Mihai ^[1]

Argonne National Lab. (ANL), Lemont, IL (United States)

Understanding power system dynamics is essential for interarea oscillation analysis and the detection of grid instabilities. The FNET/GridEye is a GPS-synchronized wide-area frequency measurement network that provides an accurate picture of the normal real-time operational condition of the power system-dynamics, giving rise to new and intricate spatiotemporal patterns of power loads. We propose to model FNET/GridEye grid frequency data from the U.S. Eastern Interconnection with a spatiotemporal statistical model. We predict the frequency data at locations without observations, a critical need during disruption events where measurement data are inaccessible. Spatial information is accounted for either as neighboring measurements in the form of covariates or with a spatiotemporal correlation model captured by a latent Gaussian field. Finally, the proposed method is useful in estimating power system dynamic response from limited phasor measurements and holds promise for predicting instability that may lead to undesirable effects such as cascading outages.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); National Science Foundation (NSF)

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1863260

Alternate ID(s):: OSTI ID: 1829943

Journal Information:: Statistical Analysis and Data Mining, Journal Name: Statistical Analysis and Data Mining Journal Issue: 6 Vol. 14; ISSN 1932-1864

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

References (30)

Separable approximations of space-time covariance matrices Genton, Marc G. Environmetrics, Vol. 18, Issue 7 https://doi.org/10.1002/env.854	journal	January 2007
Spatio-temporal modeling of particulate matter concentration through the SPDE approach Cameletti, Michela; Lindgren, Finn; Simpson, Daniel AStA Advances in Statistical Analysis, Vol. 97, Issue 2 https://doi.org/10.1007/s10182-012-0196-3	journal	May 2012
Self-adaptation of service compositions through product line reconfiguration Bashari, Mahdi; Bagheri, Ebrahim; Du, Weichang Journal of Systems and Software, Vol. 144 https://doi.org/10.1016/j.jss.2018.05.069	journal	October 2018
Spatial models with explanatory variables in the dependence structure Ingebrigtsen, Rikke; Lindgren, Finn; Steinsland, Ingelin Spatial Statistics, Vol. 8 https://doi.org/10.1016/j.spasta.2013.06.002	journal	May 2014
Does non-stationary spatial data always require non-stationary random fields? Fuglstad, Geir-Arne; Simpson, Daniel; Lindgren, Finn Spatial Statistics, Vol. 14 https://doi.org/10.1016/j.spasta.2015.10.001	journal	November 2015
Measurement‐based correlation approach for power system dynamic response estimation Bai, Feifei; Liu, Yong; Liu, Yilu IET Generation, Transmission & Distribution, Vol. 9, Issue 12 https://doi.org/10.1049/iet-gtd.2014.1013	journal	September 2015
Constructing Priors that Penalize the Complexity of Gaussian Random Fields Fuglstad, Geir-Arne; Simpson, Daniel; Lindgren, Finn Journal of the American Statistical Association, Vol. 114, Issue 525 https://doi.org/10.1080/01621459.2017.1415907	journal	July 2018
Scalable Gaussian Process Computations Using Hierarchical Matrices Geoga, Christopher J.; Anitescu, Mihai; Stein, Michael L. Journal of Computational and Graphical Statistics, Vol. 29, Issue 2 https://doi.org/10.1080/10618600.2019.1652616	journal	September 2019
A Data Imputation Model in Phasor Measurement Units Based on Bagged Averaging of Multiple Linear Regression Le, Ngoc Thien; Benjapolakul, Watit IEEE Access, Vol. 6 https://doi.org/10.1109/ACCESS.2018.2856768	journal	January 2018
Cyber-Physical Systems in the SmartGrid Karnouskos, Stamatis 2011 9th IEEE International Conference on Industrial Informatics (INDIN) https://doi.org/10.1109/INDIN.2011.6034829	conference	July 2011
Smart Grids: A Cyber–Physical Systems Perspective Yu, Xinghuo; Xue, Yusheng Proceedings of the IEEE, Vol. 104, Issue 5 https://doi.org/10.1109/JPROC.2015.2503119	journal	May 2016
Modeless reconstruction of missing synchrophasor measurements Gao, Pengzhi; Wang, Meng; Ghiocel, Scott G. 2014 IEEE PES General Meeting: Conference & Exposition, 2014 IEEE PES General Meeting \| Conference & Exposition https://doi.org/10.1109/PESGM.2014.6938973	conference	July 2014
Spatio-Temporal Synchrophasor Data Characterization for Mitigating False Data Injection in Smart Grids Cui, Yi; Wang, Weikang; Liu, Yilu 2019 IEEE Power & Energy Society General Meeting (PESGM) https://doi.org/10.1109/PESGM40551.2019.8973586	conference	August 2019
Spectrum estimation and harmonic analysis Thomson, D. J. Proceedings of the IEEE, Vol. 70, Issue 9 https://doi.org/10.1109/PROC.1982.12433	journal	January 1982
Out-of-step prediction logic for wide-area protection based on an autoregressive model Yamashita, K.; Kameda, H. IEEE PES Power Systems Conference and Exposition, 2004. https://doi.org/10.1109/PSCE.2004.1397469	conference	January 2004
Measurement based power system dynamics prediction with multivariate AutoRegressive Model Li, Changgang; Liu, Yong; Sun, Kai 2014 IEEE/PES Transmission & Distribution Conference & Exposition (T&D), 2014 IEEE PES T&D Conference and Exposition https://doi.org/10.1109/TDC.2014.6863288	conference	April 2014
Power System Dynamic State Estimation With Synchronized Phasor Measurements Aminifar, Farrokh; Shahidehpour, Mohammad; Fotuhi-Firuzabad, Mahmud IEEE Transactions on Instrumentation and Measurement, Vol. 63, Issue 2 https://doi.org/10.1109/TIM.2013.2278595	journal	February 2014
On the Optimality of Conditional Expectation as a Bregman Predictor Banerjee, A.; Guo, X.; Wang, H. IEEE Transactions on Information Theory, Vol. 51, Issue 7 https://doi.org/10.1109/TIT.2005.850145	journal	July 2005
Development of Rule-Based Classifiers for Rapid Stability Assessment of Wide-Area Post-Disturbance Records Kamwa, I.; Samantaray, S. R.; Joos, G. IEEE Transactions on Power Systems, Vol. 24, Issue 1 https://doi.org/10.1109/TPWRS.2008.2009430	journal	February 2009
Frequency Prediction of Power Systems in FNET Based on State-Space Approach and Uncertain Basis Functions Dong, Jin; Ma, Xiao; Djouadi, Seddik M. IEEE Transactions on Power Systems, Vol. 29, Issue 6 https://doi.org/10.1109/TPWRS.2014.2319057	journal	November 2014
Toward the Next Generation of Multiperiod Optimal Power Flow Solvers Kourounis, Drosos; Fuchs, Alexander; Schenk, Olaf IEEE Transactions on Power Systems, Vol. 33, Issue 4 https://doi.org/10.1109/TPWRS.2017.2789187	journal	July 2018
Predicting Critical Transitions From Time Series Synchrophasor Data Cotilla-Sanchez, Eduardo; Hines, Paul D. H.; Danforth, Christopher M. IEEE Transactions on Smart Grid, Vol. 3, Issue 4 https://doi.org/10.1109/TSG.2012.2213848	journal	December 2012
Probabilistic forecasts, calibration and sharpness Gneiting, Tilmann; Balabdaoui, Fadoua; Raftery, Adrian E. Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 69, Issue 2 https://doi.org/10.1111/j.1467-9868.2007.00587.x	journal	April 2007
Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations Rue, Håvard; Martino, Sara; Chopin, Nicolas Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 71, Issue 2 https://doi.org/10.1111/j.1467-9868.2008.00700.x	journal	April 2009
Bayesian Computing with INLA: A Review Rue, Håvard; Riebler, Andrea; Sørbye, Sigrunn H. Annual Review of Statistics and Its Application, Vol. 4, Issue 1 https://doi.org/10.1146/annurev-statistics-060116-054045	journal	March 2017
Stationary space-time Gaussian fields and their time autoregressive representation Storvik, Geir; Frigessi, Arnoldo; Hirst, David Statistical Modelling, Vol. 2, Issue 2 https://doi.org/10.1191/1471082x02st029oa	journal	July 2002
Gaussian Markov Random Fields: Theory and Applications Rue, Havard; Held, Leonhard https://doi.org/10.1201/9780203492024	book	February 2005
Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors Simpson, Daniel; Rue, Håvard; Riebler, Andrea Statistical Science, Vol. 32, Issue 1 https://doi.org/10.1214/16-STS576	journal	February 2017
Bayesian Spatial Modelling with R - INLA Lindgren, Finn; Rue, Håvard Journal of Statistical Software, Vol. 63, Issue 19 https://doi.org/10.18637/jss.v063.i19	journal	January 2015
Cyber-Physical System Security of a Power Grid: State-of-the-Art Sun, Chih-Che; Liu, Chen-Ching; Xie, Jing Electronics, Vol. 5, Issue 4 https://doi.org/10.3390/electronics5030040	journal	July 2016

Similar Records

A Distribution Level Wide Area Monitoring System for the Electric Power Grid–FNET/GridEye

Journal Article · Wed Feb 08 23:00:00 EST 2017 · IEEE Access · OSTI ID:1399393

FNETVision: A WAMS Big Data Knowledge Discovery System

Conference · Wed Aug 01 00:00:00 EDT 2018 · OSTI ID:1564166

Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
hierarchical models
phasor measurement unit
power systems
spatiotemporal correlations

Power grid frequency prediction using spatiotemporal modeling

Citation Formats

References (30)

Similar Records

Related Subjects