TY - GEN
T1 - Robust power system state estimation for the nonlinear AC flow model
AU - Zhu, Hao
AU - Giannakis, Georgios B.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Power system state estimation
KW - robustness
KW - semidefinite relaxation
KW - sparsity
KW - system identifiability
UR - http://www.scopus.com/inward/record.url?scp=84870573907&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84870573907&partnerID=8YFLogxK
U2 - 10.1109/NAPS.2012.6336405
DO - 10.1109/NAPS.2012.6336405
M3 - Conference contribution
AN - SCOPUS:84870573907
SN - 9781467323086
T3 - 2012 North American Power Symposium, NAPS 2012
BT - 2012 North American Power Symposium, NAPS 2012
T2 - 2012 North American Power Symposium, NAPS 2012
Y2 - 9 September 2012 through 11 September 2012
ER -