Harnessing Kernel Regression for Stochastic State Estimation in Solar-Integrated Power Grids

TL;DR

This paper introduces a Gaussian/kernel process regression approach for real-time stochastic state estimation and forecasting in solar-integrated power grids, effectively handling sparse measurements and high solar penetration.

Contribution

It develops a novel stochastic differential equation framework that models unknown system terms as random processes, enhancing state estimation accuracy in solar-powered grids.

Findings

Accurately estimates unobserved system states.

Improves forecast accuracy with higher observation frequency.

Performs well under measurement noise and sparse data conditions.

Abstract

The paper presents a Gaussian/kernel process regression method for real-time state estimation and forecasting of phase angle and angular speed in systems with a high penetration of solar generation units, operating under a sparse measurements regime on both sunny and cloudy days. The method treats unknown terms in the swing equations, such as solar power, as random processes, thereby transforming these equations into stochastic differential equations. The proposed method accurately forecasts and estimates both observed and unobserved operating states, delivering forecasts comparable to those of the standard data-driven Gaussian/kernel process for observed system states. Additionally, the method demonstrates improved accuracy with increased observation frequency and reduced measurement errors in the IEEE 14-bus test system.