Artificial neural network based algorithm for early prediction of transient stability using wide area measurements

Mohammed Mahdi; V. M.Istemihan Genc

doi:10.1109/SGCF.2017.7947611

Artificial neural network based algorithm for early prediction of transient stability using wide area measurements

Mohammed Mahdi
, V. M.Istemihan Genc

Elektrik Mühendisliği Bölümü

Istanbul Technical University

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakem

30 Atıf (Scopus)

Özet

Early prediction of the transient stability of power systems after fault occurrences has a great impact on the performance of wide area protection and control systems designed against transient instabilities. In this paper, an artificial neural networks based methodology is proposed for predicting the power system stability directly after clearing the fault. A dataset is generated to train a multilayer perceptron off-line, which is then used for early online prediction of any transient instability. The neural network is fed by the inputs, which are the pre-fault, during-fault, and post-fault voltage magnitude measurements collected from the phasor measurement units. The success and the effectiveness of the proposed method are demonstrated, as it is applied to the 37-generator 127-bus power test system and an accuracy above 99% is obtained in the early prediction of transient instabilities.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	ICSG 2017 - 5th International Istanbul Smart Grids and Cities Congress and Fair
Editörler	Aydin Cetin, Ali Yilmaz Camurcu
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
Sayfalar	17-21
Sayfa sayısı	5
ISBN (Elektronik)	9781509059386
DOI'lar	https://doi.org/10.1109/SGCF.2017.7947611
Yayın durumu	Yayınlandı - 13 Haz 2017
Etkinlik	5th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2017 - Istanbul, Türkiye Süre: 19 Nis 2017 → 21 Nis 2017

Yayın serisi

Adı	ICSG 2017 - 5th International Istanbul Smart Grids and Cities Congress and Fair

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

???event.eventtypes.event.conference???	5th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2017
Ülke/Bölge	Türkiye
Şehir	Istanbul
Periyot	19/04/17 → 21/04/17

Bibliyografik not

Publisher Copyright:
© 2017 IEEE.

Belgeye Erişim

10.1109/SGCF.2017.7947611

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Mahdi, M., & Genc, V. M. I. (2017). Artificial neural network based algorithm for early prediction of transient stability using wide area measurements. In A. Cetin, & A. Y. Camurcu (Eds.), ICSG 2017 - 5th International Istanbul Smart Grids and Cities Congress and Fair (pp. 17-21). Makale 7947611 (ICSG 2017 - 5th International Istanbul Smart Grids and Cities Congress and Fair). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SGCF.2017.7947611

Mahdi, Mohammed ; Genc, V. M.Istemihan. / Artificial neural network based algorithm for early prediction of transient stability using wide area measurements. ICSG 2017 - 5th International Istanbul Smart Grids and Cities Congress and Fair. editör / Aydin Cetin ; Ali Yilmaz Camurcu. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 17-21 (ICSG 2017 - 5th International Istanbul Smart Grids and Cities Congress and Fair).

@inproceedings{24ec6e48f21946288a69c5e84bdb1db7,

title = "Artificial neural network based algorithm for early prediction of transient stability using wide area measurements",

abstract = "Early prediction of the transient stability of power systems after fault occurrences has a great impact on the performance of wide area protection and control systems designed against transient instabilities. In this paper, an artificial neural networks based methodology is proposed for predicting the power system stability directly after clearing the fault. A dataset is generated to train a multilayer perceptron off-line, which is then used for early online prediction of any transient instability. The neural network is fed by the inputs, which are the pre-fault, during-fault, and post-fault voltage magnitude measurements collected from the phasor measurement units. The success and the effectiveness of the proposed method are demonstrated, as it is applied to the 37-generator 127-bus power test system and an accuracy above 99\% is obtained in the early prediction of transient instabilities.",

keywords = "Classification, Neural networks, Prediction, Transient stability, Wide area measurements",

author = "Mohammed Mahdi and Genc, \{V. M.Istemihan\}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 5th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2017 ; Conference date: 19-04-2017 Through 21-04-2017",

year = "2017",

month = jun,

day = "13",

doi = "10.1109/SGCF.2017.7947611",

language = "English",

series = "ICSG 2017 - 5th International Istanbul Smart Grids and Cities Congress and Fair",

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

pages = "17--21",

editor = "Aydin Cetin and Camurcu, \{Ali Yilmaz\}",

booktitle = "ICSG 2017 - 5th International Istanbul Smart Grids and Cities Congress and Fair",

address = "United States",

}

Mahdi, M & Genc, VMI 2017, Artificial neural network based algorithm for early prediction of transient stability using wide area measurements. in A Cetin & AY Camurcu (eds), ICSG 2017 - 5th International Istanbul Smart Grids and Cities Congress and Fair., 7947611, ICSG 2017 - 5th International Istanbul Smart Grids and Cities Congress and Fair, Institute of Electrical and Electronics Engineers Inc., pp. 17-21, 5th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2017, Istanbul, Türkiye, 19/04/17. https://doi.org/10.1109/SGCF.2017.7947611

Artificial neural network based algorithm for early prediction of transient stability using wide area measurements. / Mahdi, Mohammed; Genc, V. M.Istemihan.
ICSG 2017 - 5th International Istanbul Smart Grids and Cities Congress and Fair. ed. / Aydin Cetin; Ali Yilmaz Camurcu. Institute of Electrical and Electronics Engineers Inc., 2017. p. 17-21 7947611 (ICSG 2017 - 5th International Istanbul Smart Grids and Cities Congress and Fair).

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakem

TY - GEN

T1 - Artificial neural network based algorithm for early prediction of transient stability using wide area measurements

AU - Mahdi, Mohammed

AU - Genc, V. M.Istemihan

PY - 2017/6/13

Y1 - 2017/6/13

N2 - Early prediction of the transient stability of power systems after fault occurrences has a great impact on the performance of wide area protection and control systems designed against transient instabilities. In this paper, an artificial neural networks based methodology is proposed for predicting the power system stability directly after clearing the fault. A dataset is generated to train a multilayer perceptron off-line, which is then used for early online prediction of any transient instability. The neural network is fed by the inputs, which are the pre-fault, during-fault, and post-fault voltage magnitude measurements collected from the phasor measurement units. The success and the effectiveness of the proposed method are demonstrated, as it is applied to the 37-generator 127-bus power test system and an accuracy above 99% is obtained in the early prediction of transient instabilities.

AB - Early prediction of the transient stability of power systems after fault occurrences has a great impact on the performance of wide area protection and control systems designed against transient instabilities. In this paper, an artificial neural networks based methodology is proposed for predicting the power system stability directly after clearing the fault. A dataset is generated to train a multilayer perceptron off-line, which is then used for early online prediction of any transient instability. The neural network is fed by the inputs, which are the pre-fault, during-fault, and post-fault voltage magnitude measurements collected from the phasor measurement units. The success and the effectiveness of the proposed method are demonstrated, as it is applied to the 37-generator 127-bus power test system and an accuracy above 99% is obtained in the early prediction of transient instabilities.

KW - Classification

KW - Neural networks

KW - Prediction

KW - Transient stability

KW - Wide area measurements

UR - https://www.scopus.com/pages/publications/85023169469

U2 - 10.1109/SGCF.2017.7947611

DO - 10.1109/SGCF.2017.7947611

M3 - Conference contribution

AN - SCOPUS:85023169469

T3 - ICSG 2017 - 5th International Istanbul Smart Grids and Cities Congress and Fair

SP - 17

EP - 21

BT - ICSG 2017 - 5th International Istanbul Smart Grids and Cities Congress and Fair

A2 - Cetin, Aydin

A2 - Camurcu, Ali Yilmaz

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2017

Y2 - 19 April 2017 through 21 April 2017

ER -

Mahdi M, Genc VMI. Artificial neural network based algorithm for early prediction of transient stability using wide area measurements. In Cetin A, Camurcu AY, editörler, ICSG 2017 - 5th International Istanbul Smart Grids and Cities Congress and Fair. Institute of Electrical and Electronics Engineers Inc. 2017. p. 17-21. 7947611. (ICSG 2017 - 5th International Istanbul Smart Grids and Cities Congress and Fair). doi: 10.1109/SGCF.2017.7947611

Artificial neural network based algorithm for early prediction of transient stability using wide area measurements

Özet

Yayın serisi

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

Bibliyografik not

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap