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Study of Shipboard Power System Intelligent Network Reconfiguration

Authors: Xiuxia Yang; Yi Zhang;

Study of Shipboard Power System Intelligent Network Reconfiguration

Abstract

In this new era of the army with goals to reduce manning and increase system survivability, automatic reconfiguration of the electrical network in a shipboard power system(SPS) is critical for quickly restoring service to a section of the power system to survive battle damage. In this paper, according to the characteristics of SPS, a new system structure description matrix-enlarged associated matrix is built, which is convenient for system calculation and topology identification of SPS fault system. Combing the enlarged associated matrix, an intelligent reconfiguration methodology is presented, which using accelerated genetic algorithm, adding the new operators, such as annealing selection based on energy-entropy, immune selection and vaccination. RTDS fault restoration experiments show that the intelligent network reconfiguration algorithm can improve speed of convergence and precision of reconfiguration, while satisfying the operational requirements and priorities of loads.

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    influence
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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Average
Top 10%
Average
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