Predicting Critical Transitions From Time Series Synchrophasor Data

The dynamical behavior of power systems under stress frequently deviates from the predictions of deterministic models. Model-free methods for detecting signs of excessive stress before instability occurs would therefore be valuable. The mathematical frameworks of "fast-slow systems" and "critical slowing down" can describe the statistical behavior of dynamical systems that are subjected to random perturbations as they approach points of instability. This paper builds from existing literature on fast-slow systems to provide evidence that time series data alone can be useful to estimate the temporal distance of a power system to a critical transition, such as voltage collapse. Our method is based on identifying evidence of critical slowing down in a single stream of synchronized phasor measurements. Results from a single machine, stochastic infinite bus model, a three machine/nine bus system and the Western North American disturbance of 10 August 1996 illustrate the utility of the proposed method.