Physics-informed Machine Learning Method for Forecasting and Uncertainty Quantification of Partially Observed and Unobserved States in Power Grids

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dc.contributor.authorTartakovsky, Alexandre
dc.contributor.authorTipireddy, Ramakrishna
dc.date.accessioned2019-01-03T00:15:43Z
dc.date.available2019-01-03T00:15:43Z
dc.date.issued2019-01-08
dc.description.abstractWe present a physics-informed Gaussian Process Regression (GPR) model to predict the phase angle, angular speed, and wind mechanical power from a limited number of measurements. In the traditional data-driven GPR method, the form of the Gaussian Process auto- and cross-covariance functions is assumed and its parameters are found from measurements. In the physics-informed GPR, we treat unknown variables (including wind speed and mechanical power) as a random process and compute the auto and cross-covariance functions from the resulting stochastic power grid equations. We demonstrate that the physics-informed GPR method is significantly more accurate than the standard data-driven one for immediate forecasting of generators' angular velocity and phase angle. We also show that the physics-informed GPR provides accurate predictions of the unobserved wind mechanical power, phase angle, or angular velocity when measurements from only one of these variables are available. The immediate forecast of observed variables and predictions of unobserved variables can be used for effectively managing power grids (electricity market clearing, regulation actions) and early detection of abnormal behavior and faults. The physics-based GPR forecast time horizon depends on the combination of input (wind power, load, etc.) correlation time and characteristic (relaxation) time of the power grid and can be extended to short and medium-range times.
dc.format.extent7 pages
dc.identifier.doi10.24251/HICSS.2019.416
dc.identifier.isbn978-0-9981331-2-6
dc.identifier.urihttp://hdl.handle.net/10125/59779
dc.language.isoeng
dc.relation.ispartofProceedings of the 52nd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectIntegrating Distributed or Renewable Resources
dc.subjectElectric Energy Systems
dc.subjectMachine Learning, Forecasting, Uncertainty Quantification, Partially Observed and Unobserved States in Power Grids
dc.titlePhysics-informed Machine Learning Method for Forecasting and Uncertainty Quantification of Partially Observed and Unobserved States in Power Grids
dc.typeConference Paper
dc.type.dcmiText

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