PMU-based online change-point detection of imbalance in three-phase power systems

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

Abstract

In this paper, the problem of online change-point detection of voltage imbalance in a three-phase power system using phasor measurement unit (PMU) data is considered within a sequential hypothesis-testing framework. A general model for the positive-sequence data from a PMU measurement at the time domain and off-nominal frequencies is presented. The new formulation, which assumes an additional Gaussian noise, enables fast online detection of imbalance. Closed-form expressions of the cumulative sum (CUSUM) and generalized likelihood ratio (GLR) tests are developed for detection of imbalances. The performance of the change-point detection procedures is evaluated using the average-run-length and the expected detection delay. Numerical simulations show that the proposed method can be used for enhanced situational awareness in future grid management systems and demonstrate the ability to inform strategies for advancing grid capabilities by using change-point detection methods.

Original languageEnglish
Title of host publication2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017
ISBN (Electronic)9781538628904
DOIs
StatePublished - 26 Oct 2017
Event2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017 - Washington, United States
Duration: 23 Apr 201726 Apr 2017

Publication series

Name2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017

Conference

Conference2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017
Country/TerritoryUnited States
CityWashington
Period23/04/1726/04/17

Keywords

  • Online change-point detection
  • Phasor measurement unit (PMU)
  • Power system monitoring
  • State estimation
  • Unbalanced power system

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Control and Optimization

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