Robust state estimation based on projection statistics [of power systems]

This paper describes a fast and robust method for identifying the leverage points of a linearized power system state estimation model. These are measurements whose projections on the space spanned by the row vectors of the weighted Jacobian matrix, the so-called factor space, do not follow the patte...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on power systems 1996-05, Vol.11 (2), p.1118-1127
Main Authors: Mili, L., Cheniae, M.G., Vichare, N.S., Rousseeuw, P.J.
Format: Article
Language:English
Subjects:
Clouds
Computational efficiency
Jacobian matrices
Least squares methods
Power system measurements
Power system modeling
Projection algorithms
Robustness
State estimation
Statistics
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper describes a fast and robust method for identifying the leverage points of a linearized power system state estimation model. These are measurements whose projections on the space spanned by the row vectors of the weighted Jacobian matrix, the so-called factor space, do not follow the pattern of the bulk of the point cloud. In other words, their projections are outliers in the factor space. The proposed method is implemented through a new version of the projection algorithm that accounts for the sparsity of the Jacobian matrix. It assigns to each data point a projection statistic defined as the maximum of the standardized projections of the point cloud on some directions passing through the origin. Based on these projection statistics, a robustly weighted Schweppe-type GM-estimator is defined, which can be computed by a reweighted least squares algorithm. The computational efficiency and the robustness of the method are demonstrated on the IEEE-14 bus and the 118-bus systems.
ISSN:0885-8950
1558-0679
DOI:10.1109/59.496203