Wide Area Measurement Based Online Monitoring and Event Detection Using Convolutional Neural Networks

Mert Kesici; Can Berk Saner; Mohammed Mahdi; Yusuf Yaslan; V. M.Istemihan Genc

doi:10.1109/SGCF.2019.8782365

Wide Area Measurement Based Online Monitoring and Event Detection Using Convolutional Neural Networks

Mert Kesici
, Can Berk Saner
, Mohammed Mahdi
, Yusuf Yaslan
, V. M.Istemihan Genc

Istanbul Technical University
ISBAK Istanbul IT and Smart City Technologies Inc.

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

13 Atıf (Scopus)

Özet

Online monitoring of the power system is a vital application for enhancing the situational awareness capabilities of the system. Rapid integration of phasor measurement units in the network enables transmission system operators to analyze the events in real time due to their high reporting rates. Real-time detection and classification of the fault related events as no-fault, fault-incidence, fault-on and post-fault stage with no further disturbance, is an important requirement in order to decide on the control actions to protect the system from any instability. In this paper, a sliding window based continuous online monitoring method of the power system using convolutional neural networks is proposed. The effectiveness of the proposed method is validated on the 127-bus Western Systems Coordinating Council test system.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019 - Proceedings
Editörler	Aydin Cetin
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
Sayfalar	223-227
Sayfa sayısı	5
ISBN (Elektronik)	9781728113159
DOI'lar	https://doi.org/10.1109/SGCF.2019.8782365
Yayın durumu	Yayınlandı - Nis 2019
Etkinlik	7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019 - Istanbul, Türkiye Süre: 25 Nis 2019 → 26 Nis 2019

Yayın serisi

Adı	7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019 - Proceedings

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

???event.eventtypes.event.conference???	7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019
Ülke/Bölge	Türkiye
Şehir	Istanbul
Periyot	25/04/19 → 26/04/19

Bibliyografik not

Publisher Copyright:
© 2019 IEEE.

Finansman

ACKNOWLEDGMENT This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under grant number 118E184.

Finansörler	Finansör numarası
TUBITAK	118E184
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu

Belgeye Erişim

10.1109/SGCF.2019.8782365

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Kesici, M., Saner, C. B., Mahdi, M., Yaslan, Y., & Genc, V. M. I. (2019). Wide Area Measurement Based Online Monitoring and Event Detection Using Convolutional Neural Networks. In A. Cetin (Ed.), 7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019 - Proceedings (pp. 223-227). Makale 8782365 (7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SGCF.2019.8782365

Kesici, Mert ; Saner, Can Berk ; Mahdi, Mohammed et al. / Wide Area Measurement Based Online Monitoring and Event Detection Using Convolutional Neural Networks. 7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019 - Proceedings. editör / Aydin Cetin. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 223-227 (7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019 - Proceedings).

@inproceedings{6e2cfa7f1c75461d8460862029b4cf14,

title = "Wide Area Measurement Based Online Monitoring and Event Detection Using Convolutional Neural Networks",

abstract = "Online monitoring of the power system is a vital application for enhancing the situational awareness capabilities of the system. Rapid integration of phasor measurement units in the network enables transmission system operators to analyze the events in real time due to their high reporting rates. Real-time detection and classification of the fault related events as no-fault, fault-incidence, fault-on and post-fault stage with no further disturbance, is an important requirement in order to decide on the control actions to protect the system from any instability. In this paper, a sliding window based continuous online monitoring method of the power system using convolutional neural networks is proposed. The effectiveness of the proposed method is validated on the 127-bus Western Systems Coordinating Council test system.",

keywords = "Classification, deep learning, online monitoring, power system monitoring",

author = "Mert Kesici and Saner, \{Can Berk\} and Mohammed Mahdi and Yusuf Yaslan and Genc, \{V. M.Istemihan\}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019 ; Conference date: 25-04-2019 Through 26-04-2019",

year = "2019",

month = apr,

doi = "10.1109/SGCF.2019.8782365",

language = "English",

series = "7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019 - Proceedings",

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

pages = "223--227",

editor = "Aydin Cetin",

booktitle = "7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019 - Proceedings",

address = "United States",

}

Kesici, M, Saner, CB, Mahdi, M, Yaslan, Y & Genc, VMI 2019, Wide Area Measurement Based Online Monitoring and Event Detection Using Convolutional Neural Networks. in A Cetin (ed.), 7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019 - Proceedings., 8782365, 7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 223-227, 7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019, Istanbul, Türkiye, 25/04/19. https://doi.org/10.1109/SGCF.2019.8782365

Wide Area Measurement Based Online Monitoring and Event Detection Using Convolutional Neural Networks. / Kesici, Mert; Saner, Can Berk; Mahdi, Mohammed et al.
7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019 - Proceedings. ed. / Aydin Cetin. Institute of Electrical and Electronics Engineers Inc., 2019. p. 223-227 8782365 (7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019 - Proceedings).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - Wide Area Measurement Based Online Monitoring and Event Detection Using Convolutional Neural Networks

AU - Kesici, Mert

AU - Saner, Can Berk

AU - Mahdi, Mohammed

AU - Yaslan, Yusuf

AU - Genc, V. M.Istemihan

PY - 2019/4

Y1 - 2019/4

N2 - Online monitoring of the power system is a vital application for enhancing the situational awareness capabilities of the system. Rapid integration of phasor measurement units in the network enables transmission system operators to analyze the events in real time due to their high reporting rates. Real-time detection and classification of the fault related events as no-fault, fault-incidence, fault-on and post-fault stage with no further disturbance, is an important requirement in order to decide on the control actions to protect the system from any instability. In this paper, a sliding window based continuous online monitoring method of the power system using convolutional neural networks is proposed. The effectiveness of the proposed method is validated on the 127-bus Western Systems Coordinating Council test system.

AB - Online monitoring of the power system is a vital application for enhancing the situational awareness capabilities of the system. Rapid integration of phasor measurement units in the network enables transmission system operators to analyze the events in real time due to their high reporting rates. Real-time detection and classification of the fault related events as no-fault, fault-incidence, fault-on and post-fault stage with no further disturbance, is an important requirement in order to decide on the control actions to protect the system from any instability. In this paper, a sliding window based continuous online monitoring method of the power system using convolutional neural networks is proposed. The effectiveness of the proposed method is validated on the 127-bus Western Systems Coordinating Council test system.

KW - Classification

KW - deep learning

KW - online monitoring

KW - power system monitoring

UR - https://www.scopus.com/pages/publications/85071049394

U2 - 10.1109/SGCF.2019.8782365

DO - 10.1109/SGCF.2019.8782365

M3 - Conference contribution

AN - SCOPUS:85071049394

T3 - 7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019 - Proceedings

SP - 223

EP - 227

BT - 7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019 - Proceedings

A2 - Cetin, Aydin

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019

Y2 - 25 April 2019 through 26 April 2019

ER -

Kesici M, Saner CB, Mahdi M, Yaslan Y , Genc VMI. Wide Area Measurement Based Online Monitoring and Event Detection Using Convolutional Neural Networks. In Cetin A, editör, 7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 223-227. 8782365. (7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019 - Proceedings). doi: 10.1109/SGCF.2019.8782365

Wide Area Measurement Based Online Monitoring and Event Detection Using Convolutional Neural Networks

Özet

Yayın serisi

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

Bibliyografik not

Finansman

Belgeye Erişim

Diğer Dosyalar ve Linkler

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