Advanced Edge-Cloud Computing Framework for Automated PMU-Based Fault Localization in Distribution Networks
The detection and localization of faults plays a huge role in every electric power system, be it a transmission network (TN) or a distribution network (DN), as it ensures quick power restoration and thus enhances the system’s reliability and availability. In this paper, a framework that supports pha...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-03-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/11/7/3100 |
id |
doaj-8d2633cc458e4d40a4a705f4eb5ecb35 |
---|---|
record_format |
Article |
spelling |
doaj-8d2633cc458e4d40a4a705f4eb5ecb352021-03-31T23:03:46ZengMDPI AGApplied Sciences2076-34172021-03-01113100310010.3390/app11073100Advanced Edge-Cloud Computing Framework for Automated PMU-Based Fault Localization in Distribution NetworksDenis Sodin0Urban Rudež1Marko Mihelin2Miha Smolnikar3Andrej Čampa4Institute Jozef Stefan, Jamova 39, 1000 Ljubljana, SloveniaFaculty of Electrical Engineering, University of Ljubljana, Trzaska 25, 1000 Ljubljana, SloveniaInstitute Jozef Stefan, Jamova 39, 1000 Ljubljana, SloveniaInstitute Jozef Stefan, Jamova 39, 1000 Ljubljana, SloveniaInstitute Jozef Stefan, Jamova 39, 1000 Ljubljana, SloveniaThe detection and localization of faults plays a huge role in every electric power system, be it a transmission network (TN) or a distribution network (DN), as it ensures quick power restoration and thus enhances the system’s reliability and availability. In this paper, a framework that supports phasor measurement unit (PMU)-based fault detection and localization is presented. Besides making the process of fault detecting, localizing and reporting to the control center fully automated, the aim was to make the framework viable also for DNs, which normally do not have dedicated fiber-optic connectivity at their disposal. The quality of service (QoS) for PMU data transmission, using the widespread long-term evolution (LTE) technology, was evaluated and the conclusions of the evaluation were used in the development of the proposed edge-cloud framework. The main advantages of the proposed framework can be summarized as: (a) fault detection is performed at the edge nodes, thus bypassing communication delay and availability issues, (b) potential packet losses are eliminated by temporally storing data at the edge nodes, (c) since the detection of faults is no longer centralized, but rather takes place locally at the edge, the amount of data transferred to the control center during the steady-state conditions of the network can be significantly reduced.https://www.mdpi.com/2076-3417/11/7/3100fault detectionfault localizationedge-cloud frameworkdistribution networksmart grid |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Denis Sodin Urban Rudež Marko Mihelin Miha Smolnikar Andrej Čampa |
spellingShingle |
Denis Sodin Urban Rudež Marko Mihelin Miha Smolnikar Andrej Čampa Advanced Edge-Cloud Computing Framework for Automated PMU-Based Fault Localization in Distribution Networks Applied Sciences fault detection fault localization edge-cloud framework distribution network smart grid |
author_facet |
Denis Sodin Urban Rudež Marko Mihelin Miha Smolnikar Andrej Čampa |
author_sort |
Denis Sodin |
title |
Advanced Edge-Cloud Computing Framework for Automated PMU-Based Fault Localization in Distribution Networks |
title_short |
Advanced Edge-Cloud Computing Framework for Automated PMU-Based Fault Localization in Distribution Networks |
title_full |
Advanced Edge-Cloud Computing Framework for Automated PMU-Based Fault Localization in Distribution Networks |
title_fullStr |
Advanced Edge-Cloud Computing Framework for Automated PMU-Based Fault Localization in Distribution Networks |
title_full_unstemmed |
Advanced Edge-Cloud Computing Framework for Automated PMU-Based Fault Localization in Distribution Networks |
title_sort |
advanced edge-cloud computing framework for automated pmu-based fault localization in distribution networks |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2021-03-01 |
description |
The detection and localization of faults plays a huge role in every electric power system, be it a transmission network (TN) or a distribution network (DN), as it ensures quick power restoration and thus enhances the system’s reliability and availability. In this paper, a framework that supports phasor measurement unit (PMU)-based fault detection and localization is presented. Besides making the process of fault detecting, localizing and reporting to the control center fully automated, the aim was to make the framework viable also for DNs, which normally do not have dedicated fiber-optic connectivity at their disposal. The quality of service (QoS) for PMU data transmission, using the widespread long-term evolution (LTE) technology, was evaluated and the conclusions of the evaluation were used in the development of the proposed edge-cloud framework. The main advantages of the proposed framework can be summarized as: (a) fault detection is performed at the edge nodes, thus bypassing communication delay and availability issues, (b) potential packet losses are eliminated by temporally storing data at the edge nodes, (c) since the detection of faults is no longer centralized, but rather takes place locally at the edge, the amount of data transferred to the control center during the steady-state conditions of the network can be significantly reduced. |
topic |
fault detection fault localization edge-cloud framework distribution network smart grid |
url |
https://www.mdpi.com/2076-3417/11/7/3100 |
work_keys_str_mv |
AT denissodin advancededgecloudcomputingframeworkforautomatedpmubasedfaultlocalizationindistributionnetworks AT urbanrudez advancededgecloudcomputingframeworkforautomatedpmubasedfaultlocalizationindistributionnetworks AT markomihelin advancededgecloudcomputingframeworkforautomatedpmubasedfaultlocalizationindistributionnetworks AT mihasmolnikar advancededgecloudcomputingframeworkforautomatedpmubasedfaultlocalizationindistributionnetworks AT andrejcampa advancededgecloudcomputingframeworkforautomatedpmubasedfaultlocalizationindistributionnetworks |
_version_ |
1724177057873657856 |