Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/10251
Full metadata record
DC FieldValueLanguage
dc.contributor.authorPrusty
dc.contributor.authorBR;, Jena
dc.contributor.authorD
dc.date.accessioned2020-03-31T08:18:48Z-
dc.date.available2020-03-31T08:18:48Z-
dc.date.issued2016
dc.identifier.citationCSEE JOURNAL OF POWER AND ENERGY SYSTEMS, 2016, Vol.2, 2, pp.71-78en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/10251-
dc.description.abstractIn this paper, a probabilistic load flow analysis technique that combines the cumulant method and Gaussian mixture approximation method is proposed. This technique overcomes the incapability of the existing series expansion methods to approximate multimodal probability distributions. A mix of Gaussian, non-Gaussian, and discrete type probability distributions for input bus powers is considered. Probability distributions of multimodal bus voltages and line power flows pertaining to these inputs are precisely obtained without using any series expansion method. At the same time, multiple input correlations are considered. Performance of the proposed method is demonstrated in IEEE 14 and 57 bus test systems. Results are compared with cumulant and Gram Charlier expansion, cumulant and Cornish Fisher expansion, dependent discrete convolution, and Monte Carlo simulation. Effects of different correlation cases on distribution of bus voltages and line power flows are also studied.en_US
dc.titleCombined Cumulant and Gaussian Mixture Approximation for Correlated Probabilistic Load Flow Studies: A New Approachen_US
dc.typeArticleen_US
Appears in Collections:1. Journal Articles

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.