Variable-length event classification using PMU data with Naïve Bayes

David Foster, Xueqin Amy Liu, Mark Rafferty, David Laverty

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


Increasing levels of non-synchronous generation prompted by global emissions targets has resulted in power systems with low inertia. This has led to changing system dynamics and evolving trends in system events which are difficult to classify through traditional means. Many countries have invested in Phasor Measurement Units (PMUs) to monitor these systems over large geographical areas which form Wide Area Monitoring Systems. Due to the increased use and improved technology of PMUs this has generated vast quantities of data for system operators to process. Automatic methods for event diagnosis are required due to the complexity of system events, including variable event lengths. This paper demonstrates an approach for the wide area classification of a number of power system events. Event sequencing is used to solve the variability of event lengths. Sequential feature selection is adopted on wide-area synchronized frequency, phase angle and voltage measurements to extract the optimal features. Successful event classification is obtained by employing a Naïve Bayes classifier on the features. The reliability of this method is evaluated using simulated case studies and benchmarked against various sequence lengths.

Original languageEnglish
Title of host publicationProceedings of the 57th International Universities Power Engineering Conference, UPEC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665455053
ISBN (Print)9781665455060
Publication statusPublished - 18 Oct 2022
Event57th International Universities Power Engineering Conference: Big Data and Smart Grids - Istanbul, Turkey
Duration: 30 Aug 202202 Sept 2022

Publication series

NameInternational Universities Power Engineering Conference: Proceedings


Conference57th International Universities Power Engineering Conference: Big Data and Smart Grids
Abbreviated titleUPEC


  • Event Sequencing
  • Monte Carlo Cross-Validation
  • Naïve Bayes Classifier
  • PMU Data
  • Sequential Forward Selection
  • Variable Length

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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