Modern Fault Diagnosis in Power Systems Based on 5G Networks

annif.suggestionselectrical power networks|defects|machine learning|smart grids|mobile communication networks|distribution of electricity|interferences|data transfer|reliability (general)|data communications networks|enen
annif.suggestions.linkshttp://www.yso.fi/onto/yso/p7753|http://www.yso.fi/onto/yso/p543|http://www.yso.fi/onto/yso/p21846|http://www.yso.fi/onto/yso/p29493|http://www.yso.fi/onto/yso/p12758|http://www.yso.fi/onto/yso/p187|http://www.yso.fi/onto/yso/p544|http://www.yso.fi/onto/yso/p5429|http://www.yso.fi/onto/yso/p1629|http://www.yso.fi/onto/yso/p1957en
dc.contributor.authorSaleh, Talal
dc.contributor.facultyfi=Tekniikan ja innovaatiojohtamisen yksikkö|en=School of Technology and Innovations|-
dc.contributor.organizationfi=Vaasan yliopisto|en=University of Vaasa|
dc.date.accessioned2022-06-03T07:36:58Z
dc.date.accessioned2025-06-25T17:24:04Z
dc.date.available2022-06-03T07:36:58Z
dc.date.issued2022-05-13
dc.description.abstractThe future power system will be dynamic, requiring Intelligent control, reliable protection, and fast communication. Modern concepts in power systems, such as smart grids, involve bidirectional power flow and two-way communication. Conventional protection schemes and fault diagnosis methods are unsuitable for future power systems. This study proposes a modern fault diagnosis that integrates 5G's reliable communication and AI. 5G's URLLC, mMTC, and edge computing can bring significant advantages to the applications of power systems. In this study, a concept of intelligent fault diagnosis is proposed, which utilizes a 5G network and AI. This work is divided into two main sections. The first section develops an ML-based power system protection model in MATLAB, and the second section deals with Simulating a 5G communication network is OMNeT ++. ML algorithm developed for power system protection achieved fault detection with an accuracy of 99% and isolated faults within 7ms. The standalone 5G network without an edge computing server achieved a round trip network latency of 20 ms.-
dc.format.bitstreamtrue
dc.format.extent59-
dc.identifier.olddbid16335
dc.identifier.oldhandle10024/14251
dc.identifier.urihttps://osuva.uwasa.fi/handle/11111/11300
dc.identifier.urnURN:NBN:fi-fe2022051335487-
dc.language.isoeng-
dc.rightsCC BY 4.0-
dc.source.identifierhttps://osuva.uwasa.fi/handle/10024/14251
dc.subject.degreeprogrammeMaster´s Programme in Smart Energy-
dc.subject.disciplinefi=Sähkötekniikka|en=Electrical Engineering|-
dc.subject.ysomachine learning-
dc.subject.ysosmart grids-
dc.subject.ysomobile communication networks-
dc.subject.ysoelectrical power networks-
dc.subject.ysoMATLAB-
dc.titleModern Fault Diagnosis in Power Systems Based on 5G Networks-
dc.type.ontasotfi=Pro gradu -tutkielma|en=Master's thesis|sv=Pro gradu -avhandling|-

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Modern Fault Diagnosis in Power Systems Based on 5G Networks