NDLI: State space pruning for power system reliability evaluation using genetic algorithms

Content Provider	IEEE Xplore Digital Library
Author	Green, R.C. Lingfeng Wang Singh, C.
Copyright Year	2010
Description	Author affiliation: Department of Electrical Engineering and Computer Science at the University of Toledo, OH 43606, USA (Green, R.C.; Lingfeng Wang) \|\| Department of Electrical and Computer Engineering at the Texas A&M University, College Station, 77843, USA (Singh, C.)
Abstract	Methods have previously been developed that improve the computational efficiency and convergence of Monte Carlo simulation (MCS) when computing the reliability indices of power systems. One of these techniques works by pruning the state space in such a manner that the MCS samples a state space that has a higher density of failure states than the original state space. This paper presents a new approach to limiting the state space sampled when calculating reliability indices by pruning the state space through the use of a genetic algorithm. This paper concludes that this technique is promising to improve the computational efficiency when calculating the loss of load probability (LOLP). This is tested using two power systems: the IEEE Reliability Test System (RTS79) and the Modified Reliability Test System (MRTS).
Starting Page	1
Ending Page	6
File Size	376514
Page Count	6
File Format	PDF
ISBN	9781424465491
ISSN	19449925
e-ISBN	9781424465514
e-ISBN	9781424483570
DOI	10.1109/PES.2010.5590205
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Generators Convergence Power system reliability Reliability Biological cells Mathematical model state space pruning Genetic algorithm intelligent search reliability evaluation
Content Type	Text
Resource Type	Article

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