Estimating dynamic instability risk by measuring critical slowing down

Cascading failures typically involve a wide variety of power system dynamic phenomena, including cascading transmission line overloads, generator tripping, voltage collapse and/or rotor dynamic instability. Metrics that estimate proximity to critical points with respect to any of these phenomena could be useful as indicators of cascading failure risk. With the growing deployment of phasor measurement units (PMUs) in power systems, there is a rapidly increasing quantity of high-resolution, time-synchronized phasor data available to operators. Information in these data that could be formed into metrics of proximity to critical transition could be valuable to system operators who need to make timely, and costly, decisions to avert large blackouts. This paper provides preliminary evidence that time-series data alone, without intricate network models, can signal a pending critical transition in power systems. Our method is based on identifying “critical slowing down” in time series data. Results from a single machine stochastic infinite bus model and the Western US blackout of 10 August 1996 illustrate the utility of the proposed method.