Synchrophasor-based data mining for power system fault analysis
2012 (English)In: Innovative Smart Grid Technologies (ISGT Europe), 2012 3rd IEEE PES International Conference and Exhibition on, IEEE , 2012, 6465843- p.Conference paper (Refereed)
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.
, IEEE PES Innovative Smart Grid Technologies Conference Europe, ISSN 2165-4816
Clustering algorithm, k-Nearest Neighbor algorithm, Naïve Bayes algorithm, Phasor measurement Unit, pattern recognition, power system faults
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:kth:diva-103087DOI: 10.1109/ISGTEurope.2012.6465843ISI: 000316564100239ScopusID: 2-s2.0-84874686701ISBN: 978-146732597-4OAI: oai:DiVA.org:kth-103087DiVA: diva2:558547
2012 3rd IEEE PES Innovative Smart Grid Technologies Europe, ISGT Europe 2012; Berlin; 14 October 2012 through 17 October 2012
QC 201303252012-10-032012-10-032013-10-15Bibliographically approved