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.
Original language | English |
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Title of host publication | 7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019 - Proceedings |
Editors | Aydin Cetin |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 223-227 |
Number of pages | 5 |
ISBN (Electronic) | 9781728113159 |
DOIs | |
Publication status | Published - Apr 2019 |
Event | 7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019 - Istanbul, Turkey Duration: 25 Apr 2019 → 26 Apr 2019 |
Publication series
Name | 7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019 - Proceedings |
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Conference
Conference | 7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019 |
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Country/Territory | Turkey |
City | Istanbul |
Period | 25/04/19 → 26/04/19 |
Bibliographical note
Publisher Copyright:© 2019 IEEE.
Funding
ACKNOWLEDGMENT This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under grant number 118E184.
Funders | Funder number |
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TUBITAK | 118E184 |
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu |
Keywords
- Classification
- deep learning
- online monitoring
- power system monitoring