Paper Info
Title
Moving-horizon dynamic power system state estimation using semidefinite relaxation
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.
Year
DOI
Venue
2013
10.1109/PESGM.2014.6939925
National Harbor, MD
Keywords
Field
DocType
moving-horizon dynamic power system state estimation,dynamical state evolution model,power system control,kalman filters,static psse,mathematical programming,semidefinite relaxation,dynamic psse,power system state estimation,power system reliability,extended kalman filter,nonlinear measurement model,moving-horizon state estimation,dynamic power system state estimation,noise,vectors
Mathematical optimization,Predictability,Extended Kalman filter,Nonlinear system,Control theory,Horizon,Electric power system,Control engineering,Exploit,Dynamic demand,Mathematics,Linearization
Journal
Volume
ISSN
Citations 
abs/1312.5349
1944-9925
2
PageRank 
References 
Authors
0.45
6
3
Name
Order
Citations
PageRank
Gang Wang113616.91
Seung-Jun Kim2100362.52
Georgios B. Giannakis32123195.50