Paper Info
Title
Online semidefinite programming for power system state estimation
Abstract
Power system state estimation (PSSE) constitutes a crucial prerequisite for reliable operation of the power grid. A key challenge for accurate PSSE is the inherent nonlinearity of SCADA measurements in the system states. Recent proposals for static PSSE tackle this issue by exploiting hidden convexity structure and solving a semidefinite programming (SDP) relaxation. In this work, an online PSSE algorithm based on SDP relaxation is proposed, which enjoys a similar convexity advantage, while capitalizing on past measurements as well for improved performance. An online convex optimization technique is adopted to derive an efficient algorithm with strong performance guarantees. Numerical tests verify the efficacy of the proposed approach.
Year
DOI
Venue
2014
10.1109/ICASSP.2014.6854760
ICASSP
Keywords
Field
DocType
static psse,scada measurements,mathematical programming,scada systems,online psse algorithm,convex programming,power system state estimation,nonlinearity,online convex optimization technique,power grids,psse,hidden convexity structure,online semidefinite programming,power grid,sdp relaxation,programming,convex functions
Numerical tests,Mathematical optimization,Convexity,Nonlinear system,Computer science,Electric power system,Power grid,SCADA,Convex optimization,Semidefinite programming
Conference
ISSN
Citations 
PageRank 
1520-6149
0
0.34
References 
Authors
7
3
Name
Order
Citations
PageRank
Seung-Jun Kim1100362.52
Gang Wang29012.87
G. B. Giannakis3114641206.47