Distributed Data-Driven Algorithms for Eigen-Analysis of Power System Models

Abstract

This paper addresses the monitoring of inter-area oscillations, which pose significant risks to power system’s stability. We present distributed data-driven algorithms to estimate inter-area oscillation modes and their corresponding left and right eigenvectors. Specifically, the power system is divided into coherent areas with local estimators that process real-time PMU data. The averaged state information are exchanged over a strongly connected communication network to distributively estimate the eigenvalues of the reduced order model. By using the estimated eigenvalues and leveraging the structure of the solution to the reduced-order dynamical model, the eigenvectors are then computed via solving a least square problem in a distributed manner. Simulations conducted on a 50-bus power system model comprising four areas demonstrate that the algorithm offers a scalable and effective solution for eigenanalysis in power systems.