The test equation method for linear state estimation based on PMU data

Abstract

The advent of next generation devices for synchronized phasor measurements (voltage and current values in lines) -PMU-makes it possible to realize linear algorithms of state estimation. The paper suggests the development of the test equation technique for linear state estimation of power system facilities that are monitored on the basis of PMU measurements. New algorithms for the construction of the test equations on the basis of PMU measurements are presented. The algorithms are based on the exclusion of unmeasured variables or state vector components from the electrical circuit equations written in the rectangular coordinates. By virtue of linear nature of these equations the obtained test equations are also linear. The paper presents the algorithms for bad data detection in PMU measurements as well as the algorithms for calculation of estimates, using linear test equations which make it possible to obtain a solution within one iteration. Performance of the algorithms was tested on a test network equipped with PMUs. The developed algorithms allow us to make local state estimation of individual power system facilities (power plants, substations, network sections) that are monitored online on the basis of PMU measurements

Improving reliability of state estimation programming and computing suite based on analyzing a fault tree

“…For the SE PCS to operate under the WAMS conditions, used is a traditional algorithm of the linear state estimation through the state vector in rectangular coordinates [2]. Also, solving linear SE is successfully realized via the test equation method developed at the Melentiev Energy Systems Institute of the Siberian Branch of the Russian Academy of Sciences [3,4] that has a number of advantages over the traditional nonlinear approach [5], the main of them being a possibility to a-priori detect bad data (BD).…”

“…Also, solving linear SE is successfully realized via the test equation method developed at the Melentiev Energy Systems Institute of the Siberian Branch of the Russian Academy of Sciences [3,4] that has a number of advantages over the traditional nonlinear approach [5], the main of them being a possibility to a-priori detect bad data (BD).…”