NDLI: Composite system reliability assessment using dynamically directed Particle Swarm Optimization

Content Provider	IEEE Xplore Digital Library
Author	Benidris, M. Elsaiah, S. Mitra, J.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Michigan State Univ., East Lansing, MI, USA (Benidris, M.; Elsaiah, S.; Mitra, J.)
Abstract	This paper introduces a novel technique to evaluate composite system reliability indices using dynamically directed Binary Particle Swarm Optimization (BPSO). A key point in using BPSO in power system reliability evaluation lies in selecting the weighting factors associated with the objective function. Such weighting factors are, in fact, system dependent, thereby their appropriate values should be carefully selected in order to prevent the swarm from being trapped to one corner of the state space. In this context, the work presented here proposes a solution method to adjust such weighting factors in a dynamic fashion so that the swarm would always fly on the entire search space rather of being trapped to one corner of the search space. Therefore, in every single iteration, reliability indices are tackled and weighting factors are tuned up accordingly. The effectiveness of the proposed method has been demonstrated on the IEEE RTS. In this test case, it has been shown that the results obtained by the proposed method correspond closely to those obtained using Monte-Carlo simulation, while requiring lower computational burden.
Starting Page	1
Ending Page	6
File Size	633287
Page Count	6
File Format	PDF
ISBN	9781479912551
DOI	10.1109/NAPS.2013.6666953
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Power system reliability Reliability Indexes Monte Carlo methods Linear programming Interconnected systems Vectors Monte Carlo Simulation Directed BPSO reliability indices composite systems
Content Type	Text
Resource Type	Article

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