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Finding a closest saddle-node bifurcation in power systems: An approach by unsupervised deep learning
Linnaeus University, Faculty of Technology, Department of Physics and Electrical Engineering.ORCID iD: 0000-0002-2028-9847
Linnaeus University, Faculty of Technology, Department of Physics and Electrical Engineering.ORCID iD: 0000-0003-3111-4820
2024 (English)In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 235, article id 110632Article in journal (Refereed) Published
Abstract [en]

We propose a neural network using an unsupervised learning strategy for direct computation of closest saddle- node bifurcations, eliminating the need for labeled training data. Our method not only estimates the worst-case load increase scenarios but also significantly reduces the computational complexity traditionally associated with this task during inference time. Simulation results validate the effectiveness and real-time applicability of our approach, demonstrating its potential as a robust tool for modern power system analysis.

Place, publisher, year, edition, pages
Elsevier, 2024. Vol. 235, article id 110632
Keywords [en]
Saddle-node bifurcations, Voltage stability, Unsupervised learning, Deep learning
National Category
Computer Sciences
Research subject
Physics, Electrotechnology
Identifiers
URN: urn:nbn:se:lnu:diva-131791DOI: 10.1016/j.epsr.2024.110632ISI: 001266918000001Scopus ID: 2-s2.0-85197538486OAI: oai:DiVA.org:lnu-131791DiVA, id: diva2:1889470
Available from: 2024-08-15 Created: 2024-08-15 Last updated: 2024-09-05Bibliographically approved

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fulltext(700 kB)36 downloads
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Marcial, AlexanderPerninge, Magnus

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
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Output format
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