Identification of coherency and critical generators set in real‐time signal. Issue 18 (4th September 2017)

• Record Type: Journal Article
• Authors: Shweta, ; Kishor, Nand; Uhlen, Kjetil; Mohanty, Soumya R.
• Is Part Of: IET generation, transmission & distribution. Volume 11:Issue 18(2017)
• Page Start: 4456
• Page End: 4464
• Publication Date: 2017-09-04
• Subjects: coherence -- set theory -- phasor measurement -- power system interconnection -- time‐frequency analysis -- Wigner distribution -- signal processing -- electric generators -- power system identification; fault occurrence -- Lagrangian method -- PMUs signals -- smoothed pseudo‐Wigner–Ville distribution signal processing technique -- time–frequency coherency -- system collapse -- interconnected nonlinear system -- real system phasor measurement units -- power system network -- real‐time signal -- critical generators set -- coherency identification; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2017.0327
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : For a reliable and stable operation of a power system network, it is important to monitor the dynamics of the system. For this purpose, in real system phasor measurement units (PMUs) have been installed to give information about the grid conditions. Due to a large interconnected non‐linear system, there is a possibility of system collapse due to unusual high stress. Hence, it is essential to detect the generators going out of step at an early stage and separate them. This study presents two algorithms: first one in meant to identify the coherency between the generators in real‐time signal and second algorithm functions to quickly determine the critical generators. The algorithm for coherency identification between the generators uses time–frequency coherency based on smoothed pseudo‐Wigner–Ville distribution signal processing technique. The proposed scheme is validated on several PMUs signals. Further, the Lagrangian method is applied to detect the critical sets of generators going out of step following the severe fault occurrence in the line. This is based on the calculation of energy. The suggested approach discussed in this study can be applied to dynamically reduce a large power system.