Visualization and classification of power system frequency data streams

Jason N. Bank, Olufemi A. Omitaomu, Steven J. Fernandez, Yilu Liu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

16 Scopus citations

Abstract

Two challenges in the realization of the smart grid technology are the ability to visualize the deluge of expected data streams for global situational awareness; as well as the ability to detect disruptive and classify such events from spatially-distributed high-speed power system frequency measurements. This paper presents an interactive visualization model for high speed power system frequency data streams that displays both local and global views of the data streams for decision making process. It also presents a K-Median approach for clustering and identifying disruptive events in spatially-distributed data streams. The results from experimental evaluation on a variety of datasets show that K-Median achieve better performance and empowers analysts with the ability to make sense of a deluge of frequency measurements in a real-time situation.

Original languageEnglish
Title of host publicationICDM Workshops 2009 - IEEE International Conference on Data Mining
Pages650-655
Number of pages6
DOIs
StatePublished - 2009
Event2009 IEEE International Conference on Data Mining Workshops, ICDMW 2009 - Miami, FL, United States
Duration: Dec 6 2009Dec 6 2009

Publication series

NameICDM Workshops 2009 - IEEE International Conference on Data Mining

Conference

Conference2009 IEEE International Conference on Data Mining Workshops, ICDMW 2009
Country/TerritoryUnited States
CityMiami, FL
Period12/6/0912/6/09

Keywords

  • Classification
  • High-speed data streams
  • Power systems
  • Smart grid
  • Spatial devices
  • Visualization

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