A robust extended Kalman filter for power system dynamic state estimation using PMU measurements

Marcos Netto, Junbo Zhao, Lamine Mili

Research output: Chapter in Book/Report/Conference proceedingConference contribution

34 Scopus citations

Abstract

This paper develops a robust extended Kalman filter to estimate the rotor angles and the rotor speeds of synchronous generators of a multimachine power system. Using a batch-mode regression form, the filter processes together predicted state vector and PMU measurements to track the system dynamics faster than the standard extended Kalman filter. Our proposed filter is based on a robust GM-estimator that bounds the influence of vertical outliers and bad leverage points, which are identified by means of the projection statistics. Good statistical efficiency under the Gaussian distribution assumption of the process and the observation noise is achieved thanks to the use of the Huber cost function, which is minimized via the iteratively reweighted least squares algorithm. The asymptotic covariance matrix of the state estimation error vector is derived via the covariance matrix of the total influence function of the GM-estimator. Simulations carried out on the IEEE 39-bus test system reveal that our robust extended Kalman filter exhibits good tracking capabilities under Gaussian process and observation noise while suppressing observation outliers, even in position of leverage. These good performances are obtained only under the validity of the linear approximation of the power system model.

Original languageEnglish (US)
Title of host publication2016 IEEE Power and Energy Society General Meeting, PESGM 2016
PublisherIEEE Computer Society
ISBN (Electronic)9781509041688
DOIs
StatePublished - Nov 10 2016
Externally publishedYes
Event2016 IEEE Power and Energy Society General Meeting, PESGM 2016 - Boston, United States
Duration: Jul 17 2016Jul 21 2016

Publication series

NameIEEE Power and Energy Society General Meeting
Volume2016-November
ISSN (Print)1944-9925
ISSN (Electronic)1944-9933

Conference

Conference2016 IEEE Power and Energy Society General Meeting, PESGM 2016
Country/TerritoryUnited States
CityBoston
Period7/17/167/21/16

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Nuclear Energy and Engineering
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering

Keywords

  • Dynamic state estimation
  • Extended Kalman filter
  • Robust GM-estimator

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