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

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.<>