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DOI: 10.4230/OASIcs.Commit2Data.7
Improving Power System Resilience with Enhanced Monitoring, Control, and Protection Algorithms

Authors: Nidarshan Veerakumar, Aleksandar Boričić, Ilya Tyuryukanov, Marko Tealane, Matija Naglič, Maarten Van Riet, Danny Klaar, Arjen Jongepier, Jorrit Bos, Mohammad Golshani, Gert Rietveld, Mart van der Meijden, and Marjan Popov

Published in: OASIcs, Volume 124, Commit2Data (2024)

Abstract
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.
Cite as

Nidarshan Veerakumar, Aleksandar Boričić, Ilya Tyuryukanov, Marko Tealane, Matija Naglič, Maarten Van Riet, Danny Klaar, Arjen Jongepier, Jorrit Bos, Mohammad Golshani, Gert Rietveld, Mart van der Meijden, and Marjan Popov. Improving Power System Resilience with Enhanced Monitoring, Control, and Protection Algorithms. In Commit2Data. Open Access Series in Informatics (OASIcs), Volume 124, pp. 7:1-7:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{veerakumar_et_al:OASIcs.Commit2Data.7,
  author =	{Veerakumar, Nidarshan and Bori\v{c}i\'{c}, Aleksandar and Tyuryukanov, Ilya and Tealane, Marko and Nagli\v{c}, Matija and Van Riet, Maarten and Klaar, Danny and Jongepier, Arjen and Bos, Jorrit and Golshani, Mohammad and Rietveld, Gert and van der Meijden, Mart and Popov, Marjan},
  title =	{{Improving Power System Resilience with Enhanced Monitoring, Control, and Protection Algorithms}},
  booktitle =	{Commit2Data},
  pages =	{7:1--7:18},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-351-5},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{124},
  editor =	{Haverkort, Boudewijn R. and de Jongste, Aldert and van Kuilenburg, Pieter and Vromans, Ruben D.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Commit2Data.7},
  URN =		{urn:nbn:de:0030-drops-213647},
  doi =		{10.4230/OASIcs.Commit2Data.7},
  annote =	{Keywords: Grid Resilience, Synchrophasors, Real-time Cyber-Physical Experimental Testbed, Real-Time Monitoring, Protection, and Control, Event Detection Classification, Artificial Intelligence, Adaptive Incremental Learning, Controlled Islanding, Vulnerability, State Estimation, Dynamic Line and Cable Rating}
}
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