Abstract
Nowadays, power systems complexity requires of innovative methods to monitor and provide an adequate online assessment. Coherency identification (based on data-driven methods) is a potential tool that can be integrated into the system infrastructure for the protection and resilience of the power grid. This work presents a modification of the Koopman Mode Decomposition (KMD) by adding a sliding-window to emulate the processed system's signals and to visualise the data concentration as a Transmission System Operator (TSO). Finally, we present a study of a data-set of rotor angle observables from the Nordic 32 test system after a disturbance to observe the rapid coherency at specific time-shots. This study provides evidence that the proposed modified KMD is a fast and robust approach to analyze large time-domain simulation data.
Original language | English |
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Title of host publication | Proceedings - 2021 13th Annual IEEE Green Technologies Conference, GREENTECH 2021 |
Publisher | IEEE |
Pages | 484-488 |
Number of pages | 5 |
ISBN (Electronic) | 978-1-7281-9139-3 |
ISBN (Print) | 978-1-7281-9140-9 |
DOIs | |
Publication status | Published - Apr 2021 |
Event | 13th Annual IEEE Green Technologies (GreenTech) Conference - Online, Denver, United States Duration: 7 Apr 2021 → 9 Apr 2021 Conference number: 13 |
Publication series
Name | IEEE Green Technologies Conference |
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Volume | 2021-April |
ISSN (Electronic) | 2166-5478 |
Conference
Conference | 13th Annual IEEE Green Technologies (GreenTech) Conference |
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Country/Territory | United States |
City | Denver |
Period | 7/04/21 → 9/04/21 |
Keywords
- Koopman Mode Decomposition
- On-line Identification
- Power Systems Coherency
- Rapid Coherency
- Sliding-Window
- Wide-Area Measurement Systems
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Ecological Modelling
- Environmental Engineering