Observability method for the least median of squares estimator as applied to power systems
The formulation of an accurate data base consisting of system state variable values is an initial and critical step in the economical and secure operation of modern power systems. The Least Median of Squares (LMS) estimator is ideal in the sense that it can provide a good state estimate despite high percentages of bad data and multiple bad leverage points. The estimator is, however, computationally intensive.
In this thesis, an efficient algorithm is developed and implemented to increase the overall speed of the LMS estimator. The algorithm generates measurement samples in a manner that allows use of the resampling technique i.e., they make the system observable and also ensure that each measurement has a nearly equal probability of appearing in each of the measurement samples.