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International Journal for Uncertainty Quantification

Publicou 6 edições por ano

ISSN Imprimir: 2152-5080

ISSN On-line: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

Indexed in

UNCERTAINTY QUANTIFICATION IN DYNAMIC SIMULATIONS OF LARGE-SCALE POWER SYSTEM MODELS USING THE HIGH-ORDER PROBABILISTIC COLLOCATION METHOD ON SPARSE GRIDS

Volume 4, Edição 3, 2014, pp. 185-204
DOI: 10.1615/Int.J.UncertaintyQuantification.2013003479
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RESUMO

This paper employs a probabilistic collocation method (PCM) to quantify the uncertainties in dynamic simulations of power systems. The approach was tested on a single machine infinite bus system and the over 15,000 -bus Western Electricity Coordinating Council (WECC) system in western North America. Compared to the classic Monte Carlo (MC) method, the PCM applies the Smolyak algorithm to reduce the number of simulations that have to be performed. Therefore, the computational cost can be greatly reduced using PCM. A comparison was made with the MC method on a single machine as well as the WECC system. The simulation results show that by using PCM only a small number of sparse grid points need to be sampled even when dealing with systems with a relatively large number of uncertain parameters.

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