NDLI: Risk-based composite power system vulnerability evaluation to cascading failures using importance sampling

Content Provider	IEEE Xplore Digital Library
Author	Quan Chen Mili, L.
Copyright Year	2011
Description	Author affiliation: Bradley Department of Electrical and Computer Engineering, Northern Virginia Center, Virginia Tech, Falls Church, VA 22043, USA (Quan Chen; Mili, L.)
Abstract	Large-scale blackouts typically result from cascading failure in power systems operation. Their mitigation in power system planning calls for the development of methods and algorithms that assess the risk of cascading failures due to relay overtripping, short-circuits induced by overgrown vegetation, voltage sags, line and transformer overloading, transient instabilities, voltage collapse, to cite a few. This paper describes such a method based on composite power system reliability evaluation via sequential Monte Carlo simulation. One of the impediments of the study of these phenomena is the prohibitively large computational burden involved by the simulations. To overcome this difficulty, importance sampling technique utilizing the Weibull distribution is applied. It is shown that the method significantly reduce the number of samples that need to be investigated while maintaining the accuracy at a given level. To illustrate the developed approach, a case study is conducted and analyzed on the IEEE reliability test system.
Starting Page	1
Ending Page	6
File Size	690003
Page Count	6
File Format	PDF
ISBN	9781457710001
ISSN	19449925
e-ISBN	9781457710025
e-ISBN	9781457710018
DOI	10.1109/PES.2011.6039616
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-24
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Monte Carlo methods Power system faults Power system protection Relays Conductors Power system reliability Reliability importance sampling Composite power systems cascading failures risk assessment
Content Type	Text
Resource Type	Article

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