TY - GEN
T1 - Improving Power System Resilience with Enhanced Monitoring, Control, and Protection Algorithms
AU - Veerakumar, Nidarshan
AU - Boričić, Aleksandar
AU - Tyuryukanov, Ilya
AU - Tealane, Marko
AU - Naglič, Matija
AU - van Riet, Maarten
AU - Klaar, Danny
AU - van der Meijden, M.A.M.M.
AU - Popov, Marjan
AU - More Authors, null
PY - 2024
Y1 - 2024
N2 - This paper deals with the essentials of synchrophasor’s applications for future power systems to increase system reliability and resilience, which have been investigated within a four-year research project. The project has several applications, covering real-time disturbance detection and blackout prevention distributed across multiple work-packages. Firstly, an advanced big-data management platform built in a real-time digital simulation (RTDS) environment is described to support measurement data collection, processing, and sharing among stakeholders. This platform further presents and demonstrates a network-splitting methodology to avoid cascading failures. Online generator coherency identification is another synchrophasor application implemented on the platform, the use of which is demonstrated in the context of controlled network splitting. Using synchrophasors, data-analytics techniques can also identify and classify disturbances in real time with minor human intervention. Therefore, a novel centralized artificial intelligence (AI) based expert system is outlined to detect and classify critical events. Finally, the paper elaborates on developing advanced system resilience metrics for real-time vulnerability assessment of power systems with a high penetration of renewable energy, focusing on increasingly relevant dynamic interactions and system instability risks.
AB - This paper deals with the essentials of synchrophasor’s applications for future power systems to increase system reliability and resilience, which have been investigated within a four-year research project. The project has several applications, covering real-time disturbance detection and blackout prevention distributed across multiple work-packages. Firstly, an advanced big-data management platform built in a real-time digital simulation (RTDS) environment is described to support measurement data collection, processing, and sharing among stakeholders. This platform further presents and demonstrates a network-splitting methodology to avoid cascading failures. Online generator coherency identification is another synchrophasor application implemented on the platform, the use of which is demonstrated in the context of controlled network splitting. Using synchrophasors, data-analytics techniques can also identify and classify disturbances in real time with minor human intervention. Therefore, a novel centralized artificial intelligence (AI) based expert system is outlined to detect and classify critical events. Finally, the paper elaborates on developing advanced system resilience metrics for real-time vulnerability assessment of power systems with a high penetration of renewable energy, focusing on increasingly relevant dynamic interactions and system instability risks.
KW - Grid Resilience
KW - Synchrophasors
KW - Real-time Cyber-Physical Experimental Testbed
KW - Real-Time Monitoring
KW - Protection and Control
KW - Event Detection Classification
KW - Artificial Intelligence
KW - Adaptive Incremental Learning
KW - Controlled Islanding
KW - Vulnerability
KW - State Estimation
KW - Dynamic Line and Cable Rating
UR - http://www.scopus.com/inward/record.url?scp=85208982669&partnerID=8YFLogxK
U2 - 10.4230/OASIcs.Commit2Data.7
DO - 10.4230/OASIcs.Commit2Data.7
M3 - Conference contribution
SN - 978-3-95977-351-5
T3 - OpenAccess Series in Informatics
BT - Commit2Data
A2 - Haverkort, R.
A2 - de Jongste, A.
A2 - van Kuilenburg, P.
A2 - Vromans, R.D.
PB - Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik
T2 - Commit2Data
Y2 - 22 October 2024 through 22 October 2024
ER -
