Moving-horizon dynamic power system state estimation using semidefinite relaxation

Gang Wang, Seung Jun Kim, Georgios B. Giannakis

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

6 Citations (Scopus)

Abstract

Accurate power system state estimation (PSSE) is an essential prerequisite for reliable operation of power systems. Different from static PSSE, dynamic PSSE can exploit past measurements based on a dynamical state evolution model, offering improved accuracy and state predictability. A key challenge is the nonlinear measurement model, which is often tackled using linearization, despite divergence and local optimality issues. In this work, a moving-horizon estimation (MHE) strategy is advocated, where model nonlinearity can be accurately captured with strong performance guarantees. To mitigate local optimality, a semidefinite relaxation approach is adopted, which often provides solutions close to the global optimum. Numerical tests show that the proposed method can markedly improve upon an extended Kalman filter (EKF)-based alternative.

Original languageEnglish
Title of host publication2014 IEEE PES General Meeting / Conference and Exposition
PublisherIEEE Computer Society
EditionOctober
ISBN (Electronic)9781479964154
DOIs
Publication statusPublished - 29 Oct 2014
Event2014 IEEE Power and Energy Society General Meeting - National Harbor, United States
Duration: 27 Jul 201431 Jul 2014

Publication series

NameIEEE Power and Energy Society General Meeting
NumberOctober
Volume2014-October
ISSN (Print)1944-9925
ISSN (Electronic)1944-9933

Conference

Conference2014 IEEE Power and Energy Society General Meeting
Country/TerritoryUnited States
CityNational Harbor
Period27/07/1431/07/14

Keywords

  • Dynamic power system state estimation
  • moving-horizon state estimation
  • semidefinite relaxation

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