Development of a Least Absolute Value Power System Tracking State Estimator

The problem of determining the state of a power system is currently being solved using a number of methods, most of which are based on a least squares problem formulation. Recently, alternate methods based on linear programming have been proposed [1]. Techniques based on least absolute value (LAV) minimization, a derivative of linear programming, have shown potential for use in state estimators, especially in regards to detecting and eliminating bad data from the measurement set [2].