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Presentation
Power Quality Data Analysis Using Unsupervised Data Mining
Faculty of Informatics - Papers (Archive)
  • Ali Asheibi
  • David A Stirling, University of Wollongong
  • Sarath Perera, University of Wollongong
  • D A Robinson, University of Wollongong
RIS ID
11090
Publication Date
1-1-2004
Publication Details

Asheibi, A., Stirling, D. A., Perera, S. & Robinson, D. A. (2004). Power Quality Data Analysis Using Unsupervised Data Mining. In T. Saha (Eds.), Australasian Universities Power Engineering Conference (AUPEC 2004) Brisbane: AUPEC.

Abstract

The rapid increase in the size of databases required to store power quality monitoring data has demanded new techniques for analysing and understanding the data. One suggested technique to assist in analysis is data mining. Data mining is a process that uses a variety of data analysis tools to identify hidden patterns and relationships within large samples of data. This paper presents several data mining tools and techniques that are applicable to power quality data analysis to enable efficient reporting of disturbance indices and identify network problems through pattern recognition. This paper also presents results of data mining techniques applied to power quality data from an MV electrical distribution system to identify disturbance patterns.

Citation Information
Ali Asheibi, David A Stirling, Sarath Perera and D A Robinson. "Power Quality Data Analysis Using Unsupervised Data Mining" (2004)
Available at: http://works.bepress.com/dstirling/21/