Knowledge Discovery for Smart Grid Operation, Control, and Situation Awareness -- A Big Data Visualization Platform

Huaiguang Jiang, Yingchen Zhang, Eduard Muljadi, Yi Gu, Jun Zhang, Tianlu Gao

Research output: Contribution to conferencePaper

21 Scopus Citations


In this paper, a big data visualization platform is designed to discover the hidden useful knowledge for smart grid (SG) operation, control and situation awareness. The spawn of smart sensors at both grid side and customer side can provide large volume of heterogeneous data that collect information in all time spectrums. Extracting useful knowledge from this big-data poll is still challenging. In this paper, the Apache Spark, an open source cluster computing framework, is used to process the big-data to effectively discover the hidden knowledge. A high-speed communication architecture utilizing the Open System Interconnection (OSI) model is designed to transmit the data to a visualization platform. This visualization platform uses Google Earth, a global geographic information system (GIS) to link the geological information with the SG knowledge and visualize the information in user defined fashion. The University of Denver's campus grid is used as a SG test bench and several demonstrations are presented for the proposed platform.
Original languageAmerican English
Number of pages6
StatePublished - 2016
Event2016 North American Power Symposium (NAPS) - Denver, Colorado
Duration: 18 Sep 201620 Sep 2016


Conference2016 North American Power Symposium (NAPS)
CityDenver, Colorado

NREL Publication Number

  • NREL/CP-5D00-67825


  • Apache Spark
  • big data
  • geographic information system
  • knowledge discovery
  • parallel computation
  • smart sensor


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