Multiple bad data detection in power system state estimation using linear programming

Author

Peterson, William L. ; Girgis, Adly A.

Author_Institution

Dept. of Electr. & Comput. Eng., Clemson Univ., SC, USA

fYear

1988

fDate

0-0 1988

Firstpage

405

Lastpage

409

Abstract

The authors describe a method of identifying multiple bad measurements in power-system state estimators using linear programming. Linear programming automatically rejects bad measurements and provides an excellent set of bus voltages and angles which can be used as initial values in a weighted least-squares algorithm if filtering of Gaussian noise is desired. The performance of the linear program in the presence of multiple bad measurements is shown to be superior to the weighted-least-squares technique. The efficiency of the method is independent of the number of bad measurements and the magnitude of the error.<>

Keywords

State estimation; linear programming; measurement errors; power system analysis computing; state estimation; bus voltages; linear programming; measurement error; multiple bad data detection; power system; state estimation; weighted least-squares algorithm; Gaussian noise; Least squares approximation; Linear programming; Measurement errors; Noise measurement; Power measurement; Power system measurements; Power system reliability; Power systems; State estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

System Theory, 1988., Proceedings of the Twentieth Southeastern Symposium on

Conference_Location

Charlotte, NC, USA

ISSN

0094-2898

Print_ISBN

0-8186-0847-1

Type

conf

DOI

10.1109/SSST.1988.17085

Filename

17085