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

Department of Electrical Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

24 Citations (Scopus)

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.

Original language	English
Title of host publication	ICSG 2017 - 5th International Istanbul Smart Grids and Cities Congress and Fair
Editors	Aydin Cetin, Ali Yilmaz Camurcu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	17-21
Number of pages	5
ISBN (Electronic)	9781509059386
DOIs	https://doi.org/10.1109/SGCF.2017.7947611
Publication status	Published - 13 Jun 2017
Event	5th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2017 - Istanbul, Turkey Duration: 19 Apr 2017 → 21 Apr 2017

Publication series

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

Conference

Conference	5th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2017
Country/Territory	Turkey
City	Istanbul
Period	19/04/17 → 21/04/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

Classification
Neural networks
Prediction
Transient stability
Wide area measurements

Access to Document

10.1109/SGCF.2017.7947611

Cite this

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). Article 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. editor / 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).

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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}",

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year = "2017",

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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, Turkey, 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Mahdi, Mohammed

AU - Genc, V. M.Istemihan

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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.

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KW - Neural networks

KW - Prediction

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Mahdi M, Genc VMI. Artificial neural network based algorithm for early prediction of transient stability using wide area measurements. In Cetin A, Camurcu AY, editors, 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

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Keywords

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