Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Synchrophasor-based data mining for power system fault analysis
KTH, Skolan för elektro- och systemteknik (EES), Industriella informations- och styrsystem.
KTH, Skolan för elektro- och systemteknik (EES), Industriella informations- och styrsystem.
KTH, Skolan för elektro- och systemteknik (EES), Industriella informations- och styrsystem.ORCID-id: 0000-0003-4386-3781
KTH, Skolan för elektro- och systemteknik (EES), Industriella informations- och styrsystem.ORCID-id: 0000-0003-3014-5609
2012 (engelsk)Inngår i: Innovative Smart Grid Technologies (ISGT Europe), 2012 3rd IEEE PES International Conference and Exhibition on, IEEE , 2012, s. 6465843-Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Phasor measurement units can provide high resolution and synchronized power system data, which can be effectively utilized for the implementation of data mining techniques. Data mining, based on pattern recognition algorithms can be of significant help for power system analysis, as high definition data is often complex to comprehend. In this paper three pattern recognition algorithms are applied to perform the data mining tasks. The deployment is carried out firstly for fault data classification, secondly for checking which faults are occurring more frequently and thirdly for identifying the root cause of a fault by clustering the parameters behind each scenario. For such purposes three algorithms are chosen, k-Nearest Neighbor, Naïve Bayes and the k-means Clustering.

sted, utgiver, år, opplag, sider
IEEE , 2012. s. 6465843-
Serie
IEEE PES Innovative Smart Grid Technologies Conference Europe, ISSN 2165-4816
Emneord [en]
Clustering algorithm, k-Nearest Neighbor algorithm, Naïve Bayes algorithm, Phasor measurement Unit, pattern recognition, power system faults
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-103087DOI: 10.1109/ISGTEurope.2012.6465843ISI: 000316564100239Scopus ID: 2-s2.0-84874686701ISBN: 978-146732597-4 (tryckt)OAI: oai:DiVA.org:kth-103087DiVA, id: diva2:558547
Konferanse
2012 3rd IEEE PES Innovative Smart Grid Technologies Europe, ISGT Europe 2012; Berlin; 14 October 2012 through 17 October 2012
Forskningsfinansiär
StandUp
Merknad

QC 20130325

Tilgjengelig fra: 2012-10-03 Laget: 2012-10-03 Sist oppdatert: 2013-10-15bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekstScopus

Personposter BETA

Zhu, KunNordström, Lars

Søk i DiVA

Av forfatter/redaktør
Al Karim, MiftahChenine, MoustafaZhu, KunNordström, Lars
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric

doi
isbn
urn-nbn
Totalt: 615 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf