Skip to main navigation Skip to search Skip to main content

Electromechanical mode shape estimation based on transfer function identification using PMU measurements

  • N. Zhou
  • , Z. Huang
  • , L. Dosiek
  • , D. Trudnowski
  • , J. W. Pierre

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

43 Scopus citations

Abstract

Power system mode shapes are a key indication of how dynamic components participate in low-frequency oscillations. Traditionally, mode shapes are calculated from a linearized dynamic model. For large-scale power systems, obtaining accurate dynamic models is very difficult. Therefore, measurementbased mode shape estimation methods have certain advantages, especially for the application of real-time small signal stability monitoring. In this paper, a measurement-based mode shape identification method is proposed. The general relationship between transfer function (TF) and mode shape is derived. As an example, a least square (LS) method is implemented to estimate mode shape using an autoregressive exogenous (ARX) model. The performance of the proposed method is evaluated by Monte-Carlo studies using simulation data from a 17-machine model. The results indicate the validity of the proposed method in estimating mode shapes with reasonably good accuracy.

Original languageEnglish
Title of host publication2009 IEEE Power and Energy Society General Meeting, PES '09
DOIs
StatePublished - 2009
Event2009 IEEE Power and Energy Society General Meeting, PES '09 - Calgary, AB, Canada
Duration: Jul 26 2009Jul 30 2009

Publication series

Name2009 IEEE Power and Energy Society General Meeting, PES '09

Conference

Conference2009 IEEE Power and Energy Society General Meeting, PES '09
Country/TerritoryCanada
CityCalgary, AB
Period07/26/0907/30/09

Keywords

  • Electromechanical dynamics
  • Mode shape
  • Small signal stability
  • Synchronized phasor measurements
  • System identification

Fingerprint

Dive into the research topics of 'Electromechanical mode shape estimation based on transfer function identification using PMU measurements'. Together they form a unique fingerprint.

Cite this