Discovering Power Grid Dynamics from Data Using Low-Rank Sparse Modeling

TL;DR

This paper introduces a data-driven method combining SVD and SINDy to accurately estimate power grid dynamic parameters from time-series data, enhancing scalability and interpretability for large, low-inertia networks.

Contribution

The paper proposes the Latent-SINDy framework that reduces dimensionality and applies sparse modeling to estimate grid parameters directly from data, improving over traditional methods.

Findings

Accurately estimates inertia and damping in benchmark systems

Maintains model interpretability and reduces overfitting

Demonstrates scalability on large-scale power grids

Abstract

The growing integration of renewable energy sources has significantly reduced grid inertia, making modern power systems more vulnerable to instabilities. Accurate estimation of dynamic parameters such as inertia constants and damping coefficients is critical, yet traditional model-based methods struggle with scalability and adaptiveness in large, low-inertia networks. This paper presents a novel data-driven framework that integrates Singular Value Decomposition (SVD) with Sparse Identification of Nonlinear Dynamics (SINDy) to estimate system parameters directly from time-series data. By reducing dimensionality before applying sparse regression, the proposed Latent-SINDy (L-SINDy) method mitigates overfitting while preserving essential system dynamics. The framework is validated on IEEE benchmark systems, including the 118-bus, 300-bus, and a large-scale 2869-bus European grid. The results demonstrate an accurate recovery of the inertia and damping values, with identified models that closely match the true dynamics of the system. The approach offers scalability, interpretability, and computational efficiency, highlighting its potential for real-time monitoring and control in renewable-rich grids.