Analysis of IEC 61850-9-2LE Measured Values Using a Neural Network

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dc.contributor.authorWannous, Kinan Hasan Wafaacs
dc.contributor.authorToman, Petrcs
dc.contributor.authorJurák, Viktorcs
dc.contributor.authorWasserbauer, Vojtěchcs
dc.coverage.issue9cs
dc.coverage.volume12cs
dc.date.accessioned2020-08-04T10:59:55Z
dc.date.available2020-08-04T10:59:55Z
dc.date.issued2019-04-28cs
dc.description.abstractProcess bus communication has an important role to digitalize substations. The IEC 61850-9-2 standard specifies the requirements to transmit digital data over Ethernet networks. The paper analyses the impact of IEC 61850-9-2LE on physical protections with (analog-digital) input data of voltage and current. With the increased interaction between physical devices and communication components, the test proposes a communication analysis for a substation with the conventional method (analog input) and digital method based on the IEC 61850 standard. The use of IEC 61850 as the basis for smart grids includes the use of merging units (MUs) and deployment of relays based on microprocessors. The paper analyses the merging unit’s functions for relays using IEC 61850-9-2LE. The proposed method defines the sampled measured values source and analysis of the traffic. By using neural net pattern recognition that solves the pattern recognition problem, a relation between the inputs (number of samples/ms—interval time between the packets) and the source of the data is found. The benefit of this approach is to reduce the time to test the merging unit by getting the feedback from the merging unit and using the neural network to get the data structure of the publisher IED. Tests examine the GOOSE message and performance using the IEC standard based on a network traffic perspective.en
dc.formattextcs
dc.format.extent841-861cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationENERGIES. 2019, vol. 12, issue 9, p. 841-861.en
dc.identifier.doi10.3390/en12091618cs
dc.identifier.issn1996-1073cs
dc.identifier.other156831cs
dc.identifier.urihttp://hdl.handle.net/11012/179267
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofENERGIEScs
dc.relation.urihttps://www.mdpi.com/1996-1073/12/9/1618cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1996-1073/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectIEC61850en
dc.subjectSMVen
dc.subjectsampled valueen
dc.subjectGOOSEen
dc.subjectEtherneten
dc.subjectSVScouten
dc.subjectdelay timeen
dc.subjectIEDen
dc.subjecttime synchronizationen
dc.subjectmachine learningen
dc.subjectROCsen
dc.titleAnalysis of IEC 61850-9-2LE Measured Values Using a Neural Networken
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-156831en
sync.item.dbtypeVAVen
sync.item.insts2021.02.25 12:54:35en
sync.item.modts2021.02.25 12:14:31en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. oddělení-EEN-CVVOZEcs
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