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 language | English |
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Title of host publication | Proceedings - 2023 IEEE PES GTD International Conference and Exposition, GTD 2023 |
Editors | Mehmet Tahir Sandikkaya, Omer Usta |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 263-267 |
Number of pages | 5 |
ISBN (Electronic) | 9781728170251 |
DOIs | |
Publication status | Published - 2023 |
Event | 2023 IEEE PES Generation, Transmission and Distribution International Conference and Exposition, GTD 2023 - Istanbul, Turkey Duration: 22 May 2023 → 25 May 2023 |
Publication series
Name | Proceedings - 2023 IEEE PES GTD International Conference and Exposition, GTD 2023 |
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Conference
Conference | 2023 IEEE PES Generation, Transmission and Distribution International Conference and Exposition, GTD 2023 |
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Country/Territory | Turkey |
City | Istanbul |
Period | 22/05/23 → 25/05/23 |
Bibliographical note
Publisher Copyright:© 2023 IEEE.
Keywords
- anomaly detection
- concept drift
- smart grid
- smart grid security