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

6 Scopus citations


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
Issue numberOctober
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.


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


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