Identifying Concept Drift with Supervised Algorithms in Smart Grids

Ayse Sayin, Mostafa Mohammadpourfard, Mehmet Tahir Sandikkaya

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

2 Citations (Scopus)

Abstract

Early and correct disclosure of anomality is critical for realiable and secure activity of the smart grid. Machine learning based outlier detection schemes could be helpful when there is no concept drift in the smart grid. Concept drift is considered an unforeseeable change in the distribution of the data in machine learning [4]. In this study, an anomaly detection is formulated as a machine learning problem and a robust detection algorithm is proposed. Numerical results demonstrate effectiveness of the proposed solution to detect anomaly and contingency besides integrity attacks in the smart grid.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE PES GTD International Conference and Exposition, GTD 2023
EditorsMehmet Tahir Sandikkaya, Omer Usta
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages263-267
Number of pages5
ISBN (Electronic)9781728170251
DOIs
Publication statusPublished - 2023
Event2023 IEEE PES Generation, Transmission and Distribution International Conference and Exposition, GTD 2023 - Istanbul, Turkey
Duration: 22 May 202325 May 2023

Publication series

NameProceedings - 2023 IEEE PES GTD International Conference and Exposition, GTD 2023

Conference

Conference2023 IEEE PES Generation, Transmission and Distribution International Conference and Exposition, GTD 2023
Country/TerritoryTurkey
CityIstanbul
Period22/05/2325/05/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • anomaly detection
  • concept drift
  • smart grid
  • smart grid security

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