Efficient Computation of Power System Maximum Transient Linear Growth

TL;DR

This paper introduces a novel framework using singular value decomposition and matrix-free methods to efficiently analyze the maximum transient growth in power systems, addressing short-term stability concerns beyond traditional eigenvalue analysis.

Contribution

It presents a new approach for computing maximum transient growth in power systems, capable of handling large-scale systems efficiently with matrix-free techniques.

Findings

Validated on systems up to 70,000 buses

Accurately captures short-term growth not explained by eigenvalues

Enables analysis of large-scale power system stability

Abstract

Existing methods to determine the stability of a power system to small perturbations are based on eigenvalue analysis and focus on the asymptotic (long-term) behavior of the power grid. During the preasymptotic (short-term) transient, however, the system can exhibit large growth that is not explained by eigenvalues alone. In this paper we propose a new framework to determine the maximum (optimal) preasymptotic growth using the singular value decomposition. The approach is tailored to the analysis of quantities of interest in power system dynamics, such as the set of rotor speed deviations. Matrix-free techniques are developed to avoid the explicit formation of dense matrices and enable the analysis of large-scale systems without reaching memory bounds. Extensive results carried out from small to very large-scale systems (e.g., 70k-bus system) verify the theoretical aspects of the technique.