Synchrophasor-based Applications to Enhance Electrical System Performance in the Netherlands

Conference paper (2022)

Authors

M. Popov Intelligent Electrical Power Grids - EEMCS
Aleksandar Boricic Intelligent Electrical Power Grids - EEMCS
Nidarshan Veerakumar Intelligent Electrical Power Grids - EEMCS
Matija Naglic TenneT TSO B.V.
I. Tyuryukanov Intelligent Electrical Power Grids - EEMCS
M. Tealane Intelligent Electrical Power Grids - EEMCS
José L. Rueda Intelligent Electrical Power Grids - EEMCS
M.A.M.M. van der Meijden Intelligent Electrical Power Grids - EEMCS
P. Palensky Intelligent Electrical Power Grids - EEMCS
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Research Group
Intelligent Electrical Power Grids (EEMCS) (TU Delft)
Copyright: © 2022 M. Popov, Aleksandar Boricic, Nidarshan Veerakumar, Matija Naglic, I. Tyuryukanov, M. Tealane, José L. Rueda, M.A.M.M. van der Meijden, P. Palensky, More Authors
Artificial intelligence Algorithms Real-Time Monitoring Phasor Measurement Units Wide Area Monitoring and Protection Controlled Islanding
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Published Date

2022

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Electrical Sustainable Energy

Research Group

Intelligent Electrical Power Grids

Abstract

This paper deals with the essentials of synchrophasor applications for future power systems aimed at increasing system reliability and resilience. In this work, several applications are presented, covering real-time disturbance detection and blackout prevention. Firstly, an advanced big-data management platform built in real-time digital simulation (RTDS) environment to support measurement data collection, processing and sharing among stakeholders is described. With this platform, a network splitting methodology to avoid cascading failures is presented and demonstrated, which upon the occurrence of a disturbance successfully isolates the affected part to avoid catastrophic cascade system outage. Online generator coherency identification is another synchrophasor application implemented on the platform, whose use is demonstrated in the context of controlled network splitting. By using synchrophasors, data-analytics techniques can also be used for identifying and classifying different disturbances in real-time with the least human intervention. Therefore, a novel centralized artificial intelligence (AI) based expert system to detect and classify critical events is outlined. Finally, the paper elaborates on the development of advanced system resilience metrics for real-time vulnerability assessment, with a focus on increasingly relevant dynamic interactions between distribution and transmission systems.