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dc.contributor.authorKiel, Erlend Sandø
dc.contributor.authorJakobsen, Sigurd Hofsmo
dc.contributor.authorHaugen, Eirik
dc.contributor.authorLundemo, Sondre Duna
dc.contributor.authorRiemer-Sørensen, Signe
dc.contributor.authorRemonato, Filippo
dc.date.accessioned2023-01-26T16:39:21Z
dc.date.available2023-01-26T16:39:21Z
dc.date.created2022-07-19T11:19:52Z
dc.date.issued2022
dc.identifier.citation2022 17th International Conference on Probabilistic Methods Applied to Power Systems - PMAPS.en_US
dc.identifier.isbn978-1-6654-1211-7
dc.identifier.urihttps://hdl.handle.net/11250/3046692
dc.description.abstractAn unexpected failure or outage of one or multiple system components can cause a new operational situation that requires remedial actions. An important remedial action to model correctly is islanding. Finding the transient stability of an island is computationally heavy, and it may be necessary with a trade-off between speed and accuracy in the classification of island stability. This is especially the case if one has to perform a large number of simulations.In this paper, a decision tree based ensemble method is used to predict the stability of islands in the power system during a contingency event. A comparison study shows that the trained model can contribute with a large reduction in time spent on the transient stability assessment, while being substantially more accurate than a static power flow simulation.en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.ispartof2022 17th International Conference on Probabilistic Methods Applied to Power Systems - PMAPS
dc.subjectMachine learningen_US
dc.subjectPower system stabilityen_US
dc.subjectPower system dynamicsen_US
dc.subjectReliabilityen_US
dc.titleA tree based classifier for transient stability prediction following island splittingen_US
dc.title.alternativeA tree based classifier for transient stability prediction following island splittingen_US
dc.typeChapteren_US
dc.description.versionacceptedVersionen_US
dc.identifier.doi10.1109/PMAPS53380.2022.9810650
dc.identifier.cristin2038757
dc.relation.projectNorges forskningsråd: 294754en_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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