A Bayesian Approach to Forced Oscillation Source Location Given Uncertain Generator Parameters

TL;DR

This paper introduces a Bayesian method using a two-stage MAP optimization to locate forced oscillation sources in power systems with uncertain parameters, leveraging real-time data and an equivalent circuit model.

Contribution

It presents a novel Bayesian framework that effectively identifies oscillation sources despite parameter uncertainties and measurement noise, suitable for real-time power system applications.

Findings

Successful source localization in a 4-bus system with a single source

Effective detection of multiple sources in a 179-bus system

Robust performance under measurement noise and high uncertainty

Abstract

Since forced oscillations are exogenous to dynamic power system models, the models by themselves cannot predict when or where a forced oscillation will occur. Locating the sources of these oscillations, therefore, is a challenging problem which requires analytical methods capable of using real time power system data to trace an observed oscillation back to its source. The difficulty of this problem is exacerbated by the fact that the parameters associated with a given power system model can range from slightly uncertain to entirely unknown. In this paper, a Bayesian framework, via a two-stage Maximum A Posteriori optimization routine, is employed in order to locate the most probable source of a forced oscillation given an uncertain prior model. The approach leverages an equivalent circuit representation of the system in the frequency domain and employs a numerical procedure which makes the problem suitable for real time application. The derived framework lends itself to successful performance in the presence of PMU measurement noise, high generator parameter uncertainty, and multiple forced oscillations occurring simultaneously. The approach is tested on a 4-bus system with a single forced oscillation source and on the WECC 179-bus system with multiple oscillation sources.