Modern Fault Diagnosis in Power Systems Based on 5G Networks
| annif.suggestions | electrical power networks|defects|machine learning|smart grids|mobile communication networks|distribution of electricity|interferences|data transfer|reliability (general)|data communications networks|en | en |
| annif.suggestions.links | http://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/p1957 | en |
| dc.contributor.author | Saleh, Talal | |
| dc.contributor.faculty | fi=Tekniikan ja innovaatiojohtamisen yksikkö|en=School of Technology and Innovations| | - |
| dc.contributor.organization | fi=Vaasan yliopisto|en=University of Vaasa| | |
| dc.date.accessioned | 2022-06-03T07:36:58Z | |
| dc.date.accessioned | 2025-06-25T17:24:04Z | |
| dc.date.available | 2022-06-03T07:36:58Z | |
| dc.date.issued | 2022-05-13 | |
| dc.description.abstract | The 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.bitstream | true | |
| dc.format.extent | 59 | - |
| dc.identifier.olddbid | 16335 | |
| dc.identifier.oldhandle | 10024/14251 | |
| dc.identifier.uri | https://osuva.uwasa.fi/handle/11111/11300 | |
| dc.identifier.urn | URN:NBN:fi-fe2022051335487 | - |
| dc.language.iso | eng | - |
| dc.rights | CC BY 4.0 | - |
| dc.source.identifier | https://osuva.uwasa.fi/handle/10024/14251 | |
| dc.subject.degreeprogramme | Master´s Programme in Smart Energy | - |
| dc.subject.discipline | fi=Sähkötekniikka|en=Electrical Engineering| | - |
| dc.subject.yso | machine learning | - |
| dc.subject.yso | smart grids | - |
| dc.subject.yso | mobile communication networks | - |
| dc.subject.yso | electrical power networks | - |
| dc.subject.yso | MATLAB | - |
| dc.title | Modern Fault Diagnosis in Power Systems Based on 5G Networks | - |
| dc.type.ontasot | fi=Pro gradu -tutkielma|en=Master's thesis|sv=Pro gradu -avhandling| | - |
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