A Complex Network Based Classification of Covered Conductors Faults Detection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86100496" target="_blank" >RIV/61989100:27240/16:86100496 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27730/16:86100496 RIV/61989100:27740/16:86100496
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-319-48499-0_33" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-319-48499-0_33</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
A Complex Network Based Classification of Covered Conductors Faults Detection
Original language description
Presence of partial discharges implies the fault behavior on insulation system of medium voltage overhead lines, especially with covered conductors (CC). This paper covers the machine learning model based on features, which are derived from complex networks. These features are applied to predict whether the measured signal contains phenomenon indicating CC fault behavior or not. The comparison of different threshold levels of similarity values brings more information about complex network modeling. The final performance of the Random Forest classification algorithm shows valuable results for future research.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/LO1404" target="_blank" >LO1404: Sustainable Development of Center ENET</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Intelligent data analysis and applications: proceedings of the Third Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2016
ISBN
978-3-319-48498-3
ISSN
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e-ISSN
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Number of pages
9
Pages from-to
278-286
Publisher name
Springer
Place of publication
London
Event location
Fu-čou
Event date
Nov 7, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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