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Maximum Lyapunov Exponent Based Nearest Neighbor Algorithm For Real-Time Transient Stability Assessment
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.ORCID iD: 0000-0002-9157-4848
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.ORCID iD: 0000-0002-6431-9104
Swedish National Grid System Development, Sundbyberg, Sweden.
2024 (English)In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 234, article id 110758Article in journal (Refereed) Published
Abstract [en]

In power systems, ensuring transient stability is paramount to prevent unforeseen blackouts and power failures. Transient stability assessment is crucial for the early detection and mitigation of instabilities, providing a rapid response to severe fault situations. The concept of the maximum Lyapunov exponent facilitates fast predictions for transient stability assessment after severe disturbances. This paper introduces an efficient maximum Lyapunov exponent algorithm for online transient stability assessment, representing the primary contribution of this work. This approach uses the time series data from the rotor angles of generators or the phase angles of generator terminal buses. Case studies are conducted on the Nordic Power System, with simulations performed in DigSilent PowerFactory. This study contributes by offering insights into the performance and adaptability of the proposed algorithm.

Place, publisher, year, edition, pages
Elsevier BV , 2024. Vol. 234, article id 110758
Keywords [en]
Maximal Lyapunov exponent, Nearest neighbor algorithm, Nordic power system, Phasor measurement unit, Time domain simulation, Transient stability assessment
National Category
Control Engineering Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-349941DOI: 10.1016/j.epsr.2024.110758ISI: 001260627600001Scopus ID: 2-s2.0-85196732270OAI: oai:DiVA.org:kth-349941DiVA, id: diva2:1881725
Note

QC 20240704

Available from: 2024-07-03 Created: 2024-07-03 Last updated: 2024-07-15Bibliographically approved

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Bano, Sayyeda UmbereenGhandhari, Mehrdad

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CiteExportLink to record
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