Robust power system state estimation for the nonlinear AC flow model

Hao Zhu, Georgios B. Giannakis

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


An important monitoring task for power systems is accurate estimation of the system operation state. Under the nonlinear AC power flow model, the state estimation (SE) problem is inherently nonconvex giving rise to many local optima. In addition to nonconvexity, SE is challenged by data integrity and cyber-security issues. Unfortunately, existing robust (R-) SE schemes employed routinely in practice rely on iterative solvers, which are sensitive to initialization and cannot ensure global optimality. A novel R-SE approach is formulated here by capitalizing on the sparsity of an overcomplete outlier vector model. Observability and identifiability issues of this model are investigated, and neat links are established between R-SE and error control coding. The convex semidefinite relaxation (SDR) technique is further pursued to render the nonconvex R-SE problem efficiently solvable. The resultant algorithm markedly out-performs existing iterative alternatives, as corroborated through numerical tests on the standard IEEE 30-bus system.

Original languageEnglish (US)
Title of host publication2012 North American Power Symposium, NAPS 2012
StatePublished - Dec 10 2012
Event2012 North American Power Symposium, NAPS 2012 - Champaign, IL, United States
Duration: Sep 9 2012Sep 11 2012

Publication series

Name2012 North American Power Symposium, NAPS 2012


Other2012 North American Power Symposium, NAPS 2012
Country/TerritoryUnited States
CityChampaign, IL


  • Power system state estimation
  • robustness
  • semidefinite relaxation
  • sparsity
  • system identifiability

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Fuel Technology


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