Physics-Informed Gaussian Process Regression for States Estimation and Forecasting in Power Grids
April 06, 2024
April 06, 2024
WASHINGTON, April 6 (TNSres) -- The U.S. Department of Energy Pacific Northwest National Laboratory issued the following abstract of a journal article:
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Abstract
Real-time state estimation and forecasting is critical for efficient operation of power grids. In this paper, a physics-informed Gaussian process regression (PhI-GPR) method is presented and used for forecasting and estimating the phase angle, angular speed, and wind mechanical power of a thre . . .
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Abstract
Real-time state estimation and forecasting is critical for efficient operation of power grids. In this paper, a physics-informed Gaussian process regression (PhI-GPR) method is presented and used for forecasting and estimating the phase angle, angular speed, and wind mechanical power of a thre . . .