A bayesian framework for power system components identification

Artem Mikhalev, Alexander Emchinov, Samuel Chevalier, Yury Maximov, Petr Vorobev

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Having actual models for power system components, such as generators and loads or auxiliary equipment, is vital for a correct assessment of the power system operating state as well as establishing stability margins. Often, however, a power system operator has limited information about the actual values for power system components' parameters. Even if the model is available, its operating parameters, as well as the control settings, are time-dependent and subject to a real-time identification. Ideally, these parameters should be identified from measurement data, such as PMU signals. However, it is challenging to do this from the ambient measurements in the absence of transient dynamics since the signal to noise ratio (SNR) for such signals is not necessarily big. In this paper, we design a Bayesian framework for on-line identification of power system components' parameters based on ambient phasor measurement unit (PMU) data, that has reliable performance for SNR as low as five and for certain parameters can give good estimations even for unit SNR. Finally, we support the framework by a robust and time-efficient numerical method. We illustrate the approach efficiency on a synchronous generator example.

Original languageEnglish
Title of host publication2020 IEEE Power and Energy Society General Meeting, PESGM 2020
PublisherIEEE Computer Society
ISBN (Electronic)9781728155081
Publication statusPublished - 2 Aug 2020
Event2020 IEEE Power and Energy Society General Meeting, PESGM 2020 - Montreal, Canada
Duration: 2 Aug 20206 Aug 2020

Publication series

NameIEEE Power and Energy Society General Meeting
ISSN (Print)1944-9925
ISSN (Electronic)1944-9933


Conference2020 IEEE Power and Energy Society General Meeting, PESGM 2020


  • Parameter estimation
  • PMU measurements
  • Power system dynamics
  • Power system modeling


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