An Optimal PMU Placement Scheme for Early Prediction of Transient Instabilities in Power Systems

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

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

6 Citations (Scopus)

Abstract

Recently, technological developments in PMUs have led to a widespread deployment of PMUs in electrical power systems to carry out many online applications, thanks to their high reporting rates and synchronized measurements. Since it may not be feasible to deploy a PMU on every bus in the system, a selected number of buses can be chosen for the deployment of PMUs to achieve a certain task. In this paper, a new PMU placement method based on feature selection algorithms for obtaining a high accuracy in early predicting of transient instabilities is proposed. The optimal PMU locations are selected according to two different feature selection methods, which are filtering based and feature importance based algorithms. The feature selection methods are implemented with the use of a classifier based on LightGBM. The proposed method is demonstrated on the 127-bus Western Systems Coordinating Council test system.

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.
Pages184-188
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

    • Early prediction
    • feature selection
    • machine learning
    • PMU placement
    • transient stability

    Fingerprint

    Dive into the research topics of 'An Optimal PMU Placement Scheme for Early Prediction of Transient Instabilities in Power Systems'. Together they form a unique fingerprint.

    Cite this