Physics-informed Gaussian process regression for states estimation and forecasting in power grids

Alexandre M. Tartakovsky, Tong Ma, David A. Barajas-Solano and Ramakrishna Tipireddy

International Journal of Forecasting, 2023, vol. 39, issue 2, 967-980

Abstract: Real-time state estimation and forecasting are critical for the 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 three-generator power grid system using sparse measurements. In standard data-driven Gaussian process regression (GPR), parameterized models for the prior statistics are fit by maximizing the marginal likelihood of observed data. In the PhI-GPR method, we propose to compute the prior statistics offline by solving stochastic differential equations (SDEs) governing the power grid dynamics. The short-term forecast of a power grid system dominated by wind generation is complicated by the stochastic nature of the wind and the resulting uncertainty in wind mechanical power. Here, we assume that the power grid dynamics are governed by swing equations, with the wind mechanical power fluctuating randomly in time. We solve these equations for the mean and covariances of the power grid states using the Monte Carlo simulation method.

Keywords: Power grid; Parameter estimation; Forecast; Gaussian process regression (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:39:y:2023:i:2:p:967-980

DOI: 10.1016/j.ijforecast.2022.03.007

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