Moving-horizon dynamic power system state estimation using semidefinite relaxation

Gang Wang, Seung Jun Kim, Georgios B. Giannakis

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations

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 (US)
Article number6939925
JournalIEEE Power and Energy Society General Meeting
Volume2014-October
Issue numberOctober
DOIs
StatePublished - Oct 29 2014
Event2014 IEEE Power and Energy Society General Meeting - National Harbor, United States
Duration: Jul 27 2014Jul 31 2014

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

Keywords

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

Fingerprint Dive into the research topics of 'Moving-horizon dynamic power system state estimation using semidefinite relaxation'. Together they form a unique fingerprint.

Cite this