Distributed Real-Time Electric Power Grid Event Detection and Dynamic Characterization

Synchrophasor data generated by Phasor Measurement Units (PMUs) allow time synchronized observations of disturbances and power oscillations on an electric grid caused by sudden generation loss, topology modifications and load changes. As PMUs expose (noisy) data streams with harmonic content in excess of 30Hz sampling, automatic detection and quantification of disturbance events is essential. Furthermore, disturbance events also contain valuable information on the dynamics of the electric power grid that can be used to quantify an event by its dynamic parameters. Signal processing techniques that are currently used for event detection on the basis of PMU data are based on either a moving Discrete Fourier Transform or specially designed (linear) band pass filters that extract features from the PMU data in user-specified frequency bands. These techniques are successful in many applications in which PMU data are used to monitor electric power quality. However, these techniques require the specification of accompanying threshold levels in the user-specified frequency bands for event detection and lack the ability to quantify an event by its dynamic parameters. In this paper we show how signal processing with recursive estimation can be used to facilitate real-time detection of disturbance events (in user-specified frequency bands) by automatically adjusting the threshold levels of each PMU distributed throughout the electric grid for event detection. Once a disturbance has been detected, it is shown how the event is quantified by its dynamic parameters by estimating the oscillation frequencies and damping parameters using a realization algorithm. Computations for adjustment of threshold levels and the real-time detection of disturbance events are implemented on each PMU independently, providing a scalable solution for a network with a limited bandwidth for PMU data transmission. The real-time event detection and dynamic characterization of disturbances is illustrated on PMU data measured at the University of California, San Diego.