Wide Area Measurement-based Transient Stability Prediction using Long Short-Term Memory Networks

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)

Abstract

A fast and accurate transient stability assessment is essential for maintaining the reliability and integrity of the power system where quick corrective control actions are to be taken to prevent cascading outages. In this work, two separate classifiers based on long short-term memory networks are proposed to predict the stability status of a power system after being subjected to a credible fault. These classifiers use the immediate post-fault measurements of bus voltage magnitudes and rate of change of frequencies obtained from phasor measurement units during the first few cycles of the post-fault period. The proposed method does not assume that every bus is equipped with a PMU. It is found out that, even with only 30% of buses equipped with PMUs, the proposed classifiers are able to predict the stability status with an accuracy above 97%. Robustness of the classifiers is also investigated under noisy conditions and against the scenario of missing PMU measurements. A comparison between the two proposed classifiers is conducted by means of a t-test.

Original languageEnglish
Title of host publication7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019 - Proceedings
EditorsAydin Cetin
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages159-163
Number of pages5
ISBN (Electronic)9781728113159
DOIs
Publication statusPublished - Apr 2019
Event7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019 - Istanbul, Turkey
Duration: 25 Apr 201926 Apr 2019

Publication series

Name7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019 - Proceedings

Conference

Conference7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019
Country/TerritoryTurkey
CityIstanbul
Period25/04/1926/04/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Funding

ACKNOWLEDGMENT This work is supported by The Scientific and Technical Research Council of Turkey (TUBITAK) project no. 118E184.

FundersFunder number
TUBITAK118E184
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu

    Keywords

    • Classification
    • machine learning
    • prediction
    • transient stability
    • wide area measurements

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