Low-frequency oscillations analysis and big data

Abstract

The increased observability of processes in electric power systems, provided by the widespread introduction of synchrophasor measurement technology, as well as the use of computational processing of measurement data arrays, has led to changes in the methodology for analyzing the power system operating modes. Along with the development of new approaches to modeling processes and received measurement signals, a class of data-based methods has emerged. In a number of recent studies, the analysis of synchrophasor measurement data at the large power system scale is considered as a big data problem. Despite the controversial nature of this point of view, the application of some big data methods in synchrophasor data analysis yields positive results. In the proposed report, an assessment of the volumes of initial and intermediate data in the analysis of forced low-frequency oscillations is performed. The effect of data explosion at intermediate stages of calculations is shown. The storage and transmission of these data can be replaced by their calculation in the presence of a storable and transmittable configuration of a generalized computational scheme. A method for compact visualization of an array of oscillation dynamic parameters is shown by the example of dissipating energy. In addition, an approach is presented for configuring streams of synthetic test data at the power system scale, which provides a desired result in the analysis of dissipative energy.