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

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2 Author(s)
Peterson, W.L. ; Dept. of Electr. & Comput. Eng., Clemson Univ., SC, USA ; Girgis, A.A.

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.<>

Published in:

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

Date of Conference:

0-0 1988