Identifying Concept Drift with Supervised Algorithms in Smart Grids

Ayse Sayin; Mostafa Mohammadpourfard; Mehmet Tahir Sandikkaya

doi:10.1109/GTD49768.2023.00076

Identifying Concept Drift with Supervised Algorithms in Smart Grids

Ayse Sayin, Mostafa Mohammadpourfard, Mehmet Tahir Sandikkaya

Bilgisayar Mühendisliği Bölümü

Istanbul Technical University

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

2 Atıf (Scopus)

Özet

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.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Proceedings - 2023 IEEE PES GTD International Conference and Exposition, GTD 2023
Editörler	Mehmet Tahir Sandikkaya, Omer Usta
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
Sayfalar	263-267
Sayfa sayısı	5
ISBN (Elektronik)	9781728170251
DOI'lar	https://doi.org/10.1109/GTD49768.2023.00076
Yayın durumu	Yayınlandı - 2023
Etkinlik	2023 IEEE PES Generation, Transmission and Distribution International Conference and Exposition, GTD 2023 - Istanbul, Turkey Süre: 22 May 2023 → 25 May 2023

Yayın serisi

Adı	Proceedings - 2023 IEEE PES GTD International Conference and Exposition, GTD 2023

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	2023 IEEE PES Generation, Transmission and Distribution International Conference and Exposition, GTD 2023
Ülke/Bölge	Turkey
Şehir	Istanbul
Periyot	22/05/23 → 25/05/23

Bibliyografik not

Publisher Copyright:
© 2023 IEEE.

Belgeye Erişim

10.1109/GTD49768.2023.00076

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Sayin, A., Mohammadpourfard, M., & Sandikkaya, M. T. (2023). Identifying Concept Drift with Supervised Algorithms in Smart Grids. In M. T. Sandikkaya, & O. Usta (Eds.), Proceedings - 2023 IEEE PES GTD International Conference and Exposition, GTD 2023 (pp. 263-267). (Proceedings - 2023 IEEE PES GTD International Conference and Exposition, GTD 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GTD49768.2023.00076

Sayin, Ayse ; Mohammadpourfard, Mostafa ; Sandikkaya, Mehmet Tahir. / Identifying Concept Drift with Supervised Algorithms in Smart Grids. Proceedings - 2023 IEEE PES GTD International Conference and Exposition, GTD 2023. editör / Mehmet Tahir Sandikkaya ; Omer Usta. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 263-267 (Proceedings - 2023 IEEE PES GTD International Conference and Exposition, GTD 2023).

@inproceedings{6b359bcc2fda4b308537b64626a37db7,

title = "Identifying Concept Drift with Supervised Algorithms in Smart Grids",

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.",

keywords = "anomaly detection, concept drift, smart grid, smart grid security",

author = "Ayse Sayin and Mostafa Mohammadpourfard and Sandikkaya, {Mehmet Tahir}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE PES Generation, Transmission and Distribution International Conference and Exposition, GTD 2023 ; Conference date: 22-05-2023 Through 25-05-2023",

year = "2023",

doi = "10.1109/GTD49768.2023.00076",

language = "English",

series = "Proceedings - 2023 IEEE PES GTD International Conference and Exposition, GTD 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "263--267",

editor = "Sandikkaya, {Mehmet Tahir} and Omer Usta",

booktitle = "Proceedings - 2023 IEEE PES GTD International Conference and Exposition, GTD 2023",

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}

Sayin, A, Mohammadpourfard, M & Sandikkaya, MT 2023, Identifying Concept Drift with Supervised Algorithms in Smart Grids. in MT Sandikkaya & O Usta (eds), Proceedings - 2023 IEEE PES GTD International Conference and Exposition, GTD 2023. Proceedings - 2023 IEEE PES GTD International Conference and Exposition, GTD 2023, Institute of Electrical and Electronics Engineers Inc., pp. 263-267, 2023 IEEE PES Generation, Transmission and Distribution International Conference and Exposition, GTD 2023, Istanbul, Turkey, 22/05/23. https://doi.org/10.1109/GTD49768.2023.00076

Identifying Concept Drift with Supervised Algorithms in Smart Grids. / Sayin, Ayse; Mohammadpourfard, Mostafa; Sandikkaya, Mehmet Tahir.
Proceedings - 2023 IEEE PES GTD International Conference and Exposition, GTD 2023. ed. / Mehmet Tahir Sandikkaya; Omer Usta. Institute of Electrical and Electronics Engineers Inc., 2023. p. 263-267 (Proceedings - 2023 IEEE PES GTD International Conference and Exposition, GTD 2023).

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

TY - GEN

T1 - Identifying Concept Drift with Supervised Algorithms in Smart Grids

AU - Sayin, Ayse

AU - Mohammadpourfard, Mostafa

AU - Sandikkaya, Mehmet Tahir

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - anomaly detection

KW - concept drift

KW - smart grid

KW - smart grid security

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U2 - 10.1109/GTD49768.2023.00076

DO - 10.1109/GTD49768.2023.00076

M3 - Conference contribution

AN - SCOPUS:85175434194

T3 - Proceedings - 2023 IEEE PES GTD International Conference and Exposition, GTD 2023

SP - 263

EP - 267

BT - Proceedings - 2023 IEEE PES GTD International Conference and Exposition, GTD 2023

A2 - Sandikkaya, Mehmet Tahir

A2 - Usta, Omer

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE PES Generation, Transmission and Distribution International Conference and Exposition, GTD 2023

Y2 - 22 May 2023 through 25 May 2023

ER -

Sayin A, Mohammadpourfard M, Sandikkaya MT. Identifying Concept Drift with Supervised Algorithms in Smart Grids. In Sandikkaya MT, Usta O, editörler, Proceedings - 2023 IEEE PES GTD International Conference and Exposition, GTD 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 263-267. (Proceedings - 2023 IEEE PES GTD International Conference and Exposition, GTD 2023). doi: 10.1109/GTD49768.2023.00076

Identifying Concept Drift with Supervised Algorithms in Smart Grids

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Bibliyografik not

Belgeye Erişim

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