Anomaly detection in cyber-physical system using logistic regression analysis

Subrina Sultana Noureen, Stephen B. Bayne, Edward Shaffer, Donald Porschet, Morris Berman

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

3 Scopus citations

Abstract

The emerging smart grid, cyber-physical infrastructure, provides a steady, secure, and reliable power system over the current power grid. Synchrophasor systems, like Phasor Measurement Units (PMUs), are a key element of smart grids. They have the capability to measure time-coherent phasors of a grid. The key advantage of PMUs is the fast sampling rate that they provide over traditional Supervisory control and data acquisition (SCADA) systems which can be in the range of 30-120 samples/second. These higher sampling rates come at the cost of higher data quantities. Generating large amounts of data per day poses a challenge in making the most efficient use of information. In this paper, this problem has been addressed utilizing machine learning techniques, Logistic Regression Analysis, on PMU data. Identifying system anomalies in smart power grids is the primary focus of this paper. The standard IEEE 39 Bus system has been modified using the RT-LAB environment to generate faults and to produce synthetic synchrophasor data. Archived/offline mode data from a Phasor data concentrator (PDC) database is being used to train and test the algorithm. Additionally, the algorithm has been tested in real-time using an OPAL-RT digital real-time simulator.

Original languageEnglish
Title of host publication2019 IEEE Texas Power and Energy Conference, TPEC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538692844
DOIs
StatePublished - Mar 6 2019
Event2019 IEEE Texas Power and Energy Conference, TPEC 2019 - College Station, United States
Duration: Feb 7 2019Feb 8 2019

Publication series

Name2019 IEEE Texas Power and Energy Conference, TPEC 2019

Conference

Conference2019 IEEE Texas Power and Energy Conference, TPEC 2019
CountryUnited States
CityCollege Station
Period02/7/1902/8/19

Keywords

  • Cyber-Physical System
  • Machine Learning Algorithms
  • Phasor Measurement Unit (PMU)
  • Real-Time Digital Simulator
  • Smart Grid

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