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Activity Number:
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266
- Recent Advances in Spatial-Temporal Modeling and Its Applications
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 4, 2020 : 1:00 PM to 2:50 PM
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Sponsor:
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Korean International Statistical Society
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Abstract #312642
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Title:
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Spatio-Temporal Modeling of Power Grid Frequency Data
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Author(s):
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Amanda Lenzi* and Mihai Anitescu and Julie Bessac
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Companies:
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Argonne National Laboratory and Argonne National Laboratory and The University of Chicago and Argonne National Laboratory
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Keywords:
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spatio-tempora;
power system;
prediction
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Abstract:
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Understanding power system dynamics is essential for inter-area 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 overall real-time operational condition of the power system dynamics giving rise to new and complex spatio-temporal patterns of power loads. We propose to model grid frequency data from the US Eastern Interconnection (EI) collected at the device level with a spatio-temporal statistical model. We predict the frequency data not only where measurements are available but also at locations without observations. This information is particularly critical during disruption events where measurement data are inaccessible. The spatio-temporal correlations in our model are captured by a Gaussian field that evolves in space with a Matern covariance and in time with an autoregressive dynamical dependence. The preliminary results indicate that the proposed method is useful in estimating power system dynamic response from limited phasor measurements and holds promise for the prediction of instability caused by cascading outages.
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Authors who are presenting talks have a * after their name.
