Local Anomaly Detection by Application of Regression Analysis on PMU Data

Mark Rafferty, Paul Brogan, John Hastings, David Laverty, Xueqin Liu, Rafiullah Khan

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

3 Citations (Scopus)
1422 Downloads (Pure)


PMU data has the potential of providing a wealth of information on power system operation, health, faults and anomalies. PMU tend to provide tens of measurements per second, therefore automated anomaly detection is required; especially for use in real or near-time applications by power system operators. This paper demonstrates a method of detecting local anomalies in PMU data utilizing multiple linear regression. A window of near-time data is employed to generate a regression function that predicts the live data that arrives. If the error between the observed and predicted values exceeds a threshold an exception is noted. The threshold is dynamically updated based on the error in the regression function, allowing the method to work equally well on data of varying regularity. This anomaly detection method is not tuned to particular events and should detect novel occurrences. The method is evaluated on two numerical case studies, a genuine power system event and a man in the middle cyber attack. Real data was collected from PMUs placed on the Irish power system.
Original languageEnglish
Title of host publicationProceedings of Power and Energy Society General Meeting (PESGM), 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)978-1-5386-7703-2
Publication statusPublished - 24 Dec 2018
EventIEEE Power and Energy Society General Meeting - Portland, United States
Duration: 05 Aug 201809 Aug 2018

Publication series

NameIEEE Power & Energy Society General Meeting: Proceedings
ISSN (Print)1944-9925
ISSN (Electronic)1944-9933


ConferenceIEEE Power and Energy Society General Meeting
Abbreviated titleIEEE PES GM 2018
Country/TerritoryUnited States
Internet address

Bibliographical note

The research is supported by a British Council Newton Institutional Links Programme grant with Helwan University, Egypt.


  • Anomaly detection
  • PMU
  • Machine Learning
  • Linear regression
  • Cyber Security


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