Electromechanical Wave Green's Function Estimation from Ambient Electrical Grid Frequency Noise

Abstract

Many electrical grid transients can be described by the propagation of electromechanical (EM) waves that couple oscillations of power flows over transmission lines and the inertia of synchronous generators. These EM waves can take several forms: large-scale standing waves forming inter-area modes, localized oscillations of single or multi-machine modes, or traveling waves that spread quasi-circularly from major grid disturbances. The propagation speed and damping of these EM waves are potentially a powerful tool for assessing grid stability, e.g. small signal or rotor angle stability, however, EM wave properties have been mostly extracted from post-event analysis of major grid disturbances. Using a small set of data from the FNET sensor network, we show how the spatially resolved Green's function for EM wave propagation can be extracted from ambient frequency noise without the need for a major disturbance. If applied to an entire interconnection, an EM-wave Green's function map will enable a modelindependent method of predicting the propagation of grid disturbances and assessing stability.

A Dynamic Response Recovery Framework Using Ambient Synchrophasor Data

“…1]. Due to correlated failures of multiple components under e.g., extreme weather conditions, power imbalance can propagate through a wide-area interconnected grid as result of the so-termed electro-mechanical (EM) frequency oscillations [2], [3]. If the oscillation modes are poorly damped, a small input disturbance could trigger loss of synchronization and even cascading outages at other areas, such as the 1996 US/Canada Western Interconnection blackout and the 2002 Italian blackout; see e.g., [4].…”

Revealing Disturbance Localization Issues in Large Power Systems

“…On the other hand, in reality both z and h vary from one part of the system to another, which is the reason for an emerging need for alternative localization approaches. These approaches vary from mathematically exact yet complex [12] which makes them less suitable for practical implementation up to more practically oriented [13]. However, both require profound understanding of the phenomenon [14].…”