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

Samuel Chevalier, Petr Vorobev, Konstantin Turitsyn

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

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 phasor measurement unit measurement noise, high generator parameter uncertainty, and multiple forced oscillations occurring simultaneously. The approach is tested on a four-bus system with a single forced oscillation source and on the WECC 179-bus system with multiple oscillation sources.

Original languageEnglish
Article number8519314
Pages (from-to)1641-1649
Number of pages9
JournalIEEE Transactions on Power Systems
Volume34
Issue number2
DOIs
Publication statusPublished - Mar 2019

Keywords

  • Bayesian analysis
  • forced oscillations
  • inverse problems
  • low frequency oscillations
  • parameter estimation, phasor measurement unit (PMU)
  • power system dynamics

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