Distribution system event detection and classification using local voltage measurements

Desiree Phillips, Thomas Overbye

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Wide-Area Measurement Systems (WAMS) are being implemented in order to help increase situational awareness within the electric grid. These systems use Phasor Measurement Units (PMUs), devices which can measure the voltage and current within the power system. One kind of PMU is the Frequency Disturbance Recorder (FDR), which measures voltage magnitude, frequency, and phase angle at 10 data samples per second. These measurements are taken at the 120V level, resulting in a relatively low-cost, rapidly deployable alternative to other PMUs. This paper presents an approach to distribution system event detection and classification using voltage data obtained from FDRs installed around the University of Illinois at Urbana-Champaign (UIUC). This model-free classification of events will use pattern recognition techniques to help identify features that may be unique to these disturbances. Analysis will be applied to a sliding window of voltage data, and the results from each window are compared against one another in order to help determine what kind of event occurred.

Original languageEnglish (US)
Title of host publication2014 IEEE Power and Energy Conference at Illinois, PECI 2014
PublisherIEEE Computer Society
DOIs
StatePublished - 2014
Event2014 IEEE Power and Energy Conference at Illinois, PECI 2014 - Champaign, IL, United States
Duration: Feb 28 2014Mar 1 2014

Other

Other2014 IEEE Power and Energy Conference at Illinois, PECI 2014
CountryUnited States
CityChampaign, IL
Period2/28/143/1/14

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Keywords

  • Distribution System
  • Frequency Disturbance Recorder
  • Pattern Recognition
  • Power

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Fuel Technology

Cite this

Phillips, D., & Overbye, T. (2014). Distribution system event detection and classification using local voltage measurements. In 2014 IEEE Power and Energy Conference at Illinois, PECI 2014 [6804576] IEEE Computer Society. https://doi.org/10.1109/PECI.2014.6804576