Identifying System-Wide Early Warning Signs of Instability in Stochastic Power Systems

TL;DR

This paper introduces a method using spectral participation factors from the reduced power flow Jacobian to predict system-wide voltage instability and identify critical transition points in stochastic power systems.

Contribution

It demonstrates that participation factors can predict bus voltage variance and locate generators nearing critical transitions, enhancing early warning capabilities.

Findings

Participation factors predict bus voltage variance.

Method effectively locates generators near critical transitions.

Validated on a 2383-bus test case.

Abstract

Prior research has shown that spectral decomposition of the reduced power flow Jacobian (RPFJ) can yield participation factors that describe the extent to which particular buses contribute to particular spectral components of a power system. Research has also shown that both variance and autocorrelation of time series voltage data tend to increase as a power system with stochastically fluctuating loads approaches certain critical transitions. This paper presents evidence suggesting that a system's participation factors predict the relative bus voltage variance values for all nodes in a system. As a result, these participation factors can be used to filter, weight, and combine real time PMU data from various locations dispersed throughout a power network in order to develop coherent measures of global voltage stability. This paper first describes the method of computing the participation factors. Next, two potential uses of the participation factors are given: (1) predicting the relative bus voltage variance magnitudes, and (2) locating generators at which the autocorrelation of voltage measurements clearly indicate proximity to critical transitions. The methods are tested using both analytical and numerical results from a dynamic model of a 2383-bus test case.