An efficient multifidelity model for assessing risk probabilities in power systems under rare events

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dc.contributor.authorXu, Yijunen
dc.contributor.authorKorkali, Merten
dc.contributor.authorMili, Lamine M.en
dc.contributor.authorChen, Xiaoen
dc.date.accessioned2024-01-22T14:50:13Zen
dc.date.available2024-01-22T14:50:13Zen
dc.date.issued2020en
dc.description.abstractRisk assessment of power system failures induced by low-frequency, high-impact rare events is of paramount importance to power system planners and operators. In this paper, we develop a cost-effective multi-surrogate method based on multifidelity model for assessing risks in probabilistic power-flow analysis under rare events. Specifically, multiple polynomial-chaos-expansion-based surrogate models are constructed to reproduce power system responses to the stochastic changes of the load and the random occurrence of component outages. These surrogates then propagate a large number of samples at negligible computation cost and thus efficiently screen out the samples associated with high-risk rare events. The results generated by the surrogates, however, may be biased for the samples located in the low-probability tail regions that are critical to power system risk assessment. To resolve this issue, the original high-fidelity power system model is adopted to fine-tune the estimation results of low-fidelity surrogates by reevaluating only a small portion of the samples. This multifidelity model approach greatly improves the computational efficiency of the traditional Monte Carlo method used in computing the risk-event probabilities under rare events without sacrificing computational accuracy.en
dc.description.versionPublished versionen
dc.format.extentPages 3127-3136en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.24251/hicss.2020.381en
dc.identifier.isbn9780998133133en
dc.identifier.issn1530-1605en
dc.identifier.orcidMili, Lamine [0000-0001-6134-3945]en
dc.identifier.urihttps://hdl.handle.net/10919/117514en
dc.identifier.volume2020-Januaryen
dc.language.isoenen
dc.publisherHawaii International Conference on System Sciencesen
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.titleAn efficient multifidelity model for assessing risk probabilities in power systems under rare eventsen
dc.title.serialProceedings of the Annual Hawaii International Conference on System Sciencesen
dc.typeConference proceedingen
dc.type.dcmitypeTexten
dc.type.otherConference Proceedingen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Electrical and Computer Engineeringen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen

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