Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Synchrophasor-based data mining for power system fault analysis
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.ORCID iD: 0000-0003-4386-3781
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.ORCID iD: 0000-0003-3014-5609
2012 (English)In: Innovative Smart Grid Technologies (ISGT Europe), 2012 3rd IEEE PES International Conference and Exhibition on, IEEE , 2012, 6465843- p.Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
IEEE , 2012. 6465843- p.
Series
IEEE PES Innovative Smart Grid Technologies Conference Europe, ISSN 2165-4816
Keyword [en]
Clustering algorithm, k-Nearest Neighbor algorithm, Naïve Bayes algorithm, Phasor measurement Unit, pattern recognition, power system faults
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-103087DOI: 10.1109/ISGTEurope.2012.6465843ISI: 000316564100239Scopus ID: 2-s2.0-84874686701ISBN: 978-146732597-4 (print)OAI: oai:DiVA.org:kth-103087DiVA: diva2:558547
Conference
2012 3rd IEEE PES Innovative Smart Grid Technologies Europe, ISGT Europe 2012; Berlin; 14 October 2012 through 17 October 2012
Funder
StandUp
Note

QC 20130325

Available from: 2012-10-03 Created: 2012-10-03 Last updated: 2013-10-15Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Authority records BETA

Zhu, KunNordström, Lars

Search in DiVA

By author/editor
Al Karim, MiftahChenine, MoustafaZhu, KunNordström, Lars
By organisation
Industrial Information and Control Systems
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 387 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf