Locating the source of forced oscillations in transmission power grids

TL;DR

This paper introduces a new method for locating the source of forced oscillations in power grids by using a maximum likelihood approach for dynamic model identification, even with limited system parameter knowledge.

Contribution

The paper presents a novel technique for source localization of forced oscillations that does not require detailed system parameters, improving accuracy in complex power grid scenarios.

Findings

The method successfully identifies oscillation sources in various resonance scenarios.

Accurate source localization is possible without full system parameter knowledge.

The approach is applicable to other dynamical systems with linearized dynamics.

Abstract

Forced oscillation event in power grids refers to a state where malfunctioning or abnormally operating equipment causes persisting periodic disturbances in the system. While power grids are designed to damp most of perturbations during standard operations, some of them can excite normal modes of the system and cause significant energy transfers across the system, creating large oscillations thousands of miles away from the source. Localization of the source of such disturbances remains an outstanding challenge due to a limited knowledge of the system parameters outside of the zone of responsibility of system operators. Here, we propose a new method for locating the source of forced oscillations which addresses this challenge by performing a simultaneous dynamic model identification using a principled maximum likelihood approach. We illustrate the validity of the algorithm on a variety of examples where forcing leads to resonance conditions in the system dynamics. Our results establish that an accurate knowledge of system parameters is not required for a successful inference of the source and frequency of a forced oscillation. We anticipate that our method will find a broader application in general dynamical systems that can be well-described by their linearized dynamics over short periods of time.