Robust Statistical Methods for Measurement calibration in Large Electric Power Systems

Abstract

The Objective of the Remote Measurements Calibration (RMC) method is to minimize systematic errors through an appropriate scaling procedure. A new method for RMC has been developed. This method solves the problems of observability, multiplicity of solutions, and ambiguity of reference points associated with the method proposed by Adibi et. al. [6-9]. The new algorithm uses the simulated annealing technique together with the matroid method to identify and minimize the number of RTUs (Remote Terminal Units) required to observe the system. After field calibration, these RTUs provide measurements that are used to estimate the whole state of the system. These estimates are then returned as a reference for remotely calibrating the remaining RTUs. The calibration coefficients are estimated by means of highly robust estimator, namely the Least Median of Squares (LMS) estimator. The calibration method is applicable to large systems by means of network tearing and dynamic programming. The number of field calibrations can be decreased further whenever multiple voltage measurements at the same buses are available. The procedure requires that the measurement biases are estimated from recorded metered values when buses, or lines, or transformers are disconnected. It also requires the application of a robust comparative voltage calibration method. To this end, a modified Friedman test has been developed and its robustness characteristics investigated.