Overall fault identification results.
<div><p>Power transformers are essential elements in power systems and thus their protection schemes have critical importance. In this paper, a scheme is proposed for accurate discrimination and location of internal faults in power transformers using conventional measuring devices attach...
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2024
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| _version_ | 1852025993708961792 |
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| author | Mohammed Youssef (8691402) |
| author2 | El-Said Abdelaziz (19838093) Hassan Saad (14122039) Mohammed Attia (19838096) |
| author2_role | author author author |
| author_facet | Mohammed Youssef (8691402) El-Said Abdelaziz (19838093) Hassan Saad (14122039) Mohammed Attia (19838096) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mohammed Youssef (8691402) El-Said Abdelaziz (19838093) Hassan Saad (14122039) Mohammed Attia (19838096) |
| dc.date.none.fl_str_mv | 2024-10-11T17:30:08Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0309926.t004 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Overall_fault_identification_results_/27213452 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biotechnology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified artificial neural network transformer healthy condition obtained results validate div >< p different internal faults internal winding faults internal faults disk faults different types xlink "> protection schemes power transformers power transformer power systems physical modification partial discharge new approach locus diagram intensely considered input voltage input current fault inside extensively examined essential elements developed locus critical importance axial displacement accurately distinguish accurate discrimination |
| dc.title.none.fl_str_mv | Overall fault identification results. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>Power transformers are essential elements in power systems and thus their protection schemes have critical importance. In this paper, a scheme is proposed for accurate discrimination and location of internal faults in power transformers using conventional measuring devices attached to the transformer. Different types of internal winding faults are intensely considered: partial discharge, inter-disk faults, series and shunt short circuit faults and axial displacement. Depending on the transformer measured output voltage, input voltage and the input current, the construction of a locus diagram (ΔV-I<sub>in</sub>) serves as an indicator for any physical modification to the winding. Using five suggested features extracted from the developed locus, an artificial neural network (ANN) technique is applied to accurately distinguish any deviation from the transformer healthy condition. The exact location of each fault inside the windings of power transformer is then determined. The obtained results validate the usefulness of the proposed scheme for different internal faults. The superiority of the proposed scheme is extensively examined by comparing its results with some published schemes.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_4e2aa28d261eddbe36f77aae1fd148bb |
| identifier_str_mv | 10.1371/journal.pone.0309926.t004 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/27213452 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Overall fault identification results.Mohammed Youssef (8691402)El-Said Abdelaziz (19838093)Hassan Saad (14122039)Mohammed Attia (19838096)BiotechnologySpace ScienceBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedartificial neural networktransformer healthy conditionobtained results validatediv >< pdifferent internal faultsinternal winding faultsinternal faultsdisk faultsdifferent typesxlink ">protection schemespower transformerspower transformerpower systemsphysical modificationpartial dischargenew approachlocus diagramintensely consideredinput voltageinput currentfault insideextensively examinedessential elementsdeveloped locuscritical importanceaxial displacementaccurately distinguishaccurate discrimination<div><p>Power transformers are essential elements in power systems and thus their protection schemes have critical importance. In this paper, a scheme is proposed for accurate discrimination and location of internal faults in power transformers using conventional measuring devices attached to the transformer. Different types of internal winding faults are intensely considered: partial discharge, inter-disk faults, series and shunt short circuit faults and axial displacement. Depending on the transformer measured output voltage, input voltage and the input current, the construction of a locus diagram (ΔV-I<sub>in</sub>) serves as an indicator for any physical modification to the winding. Using five suggested features extracted from the developed locus, an artificial neural network (ANN) technique is applied to accurately distinguish any deviation from the transformer healthy condition. The exact location of each fault inside the windings of power transformer is then determined. The obtained results validate the usefulness of the proposed scheme for different internal faults. The superiority of the proposed scheme is extensively examined by comparing its results with some published schemes.</p></div>2024-10-11T17:30:08ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0309926.t004https://figshare.com/articles/dataset/Overall_fault_identification_results_/27213452CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/272134522024-10-11T17:30:08Z |
| spellingShingle | Overall fault identification results. Mohammed Youssef (8691402) Biotechnology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified artificial neural network transformer healthy condition obtained results validate div >< p different internal faults internal winding faults internal faults disk faults different types xlink "> protection schemes power transformers power transformer power systems physical modification partial discharge new approach locus diagram intensely considered input voltage input current fault inside extensively examined essential elements developed locus critical importance axial displacement accurately distinguish accurate discrimination |
| status_str | publishedVersion |
| title | Overall fault identification results. |
| title_full | Overall fault identification results. |
| title_fullStr | Overall fault identification results. |
| title_full_unstemmed | Overall fault identification results. |
| title_short | Overall fault identification results. |
| title_sort | Overall fault identification results. |
| topic | Biotechnology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified artificial neural network transformer healthy condition obtained results validate div >< p different internal faults internal winding faults internal faults disk faults different types xlink "> protection schemes power transformers power transformer power systems physical modification partial discharge new approach locus diagram intensely considered input voltage input current fault inside extensively examined essential elements developed locus critical importance axial displacement accurately distinguish accurate discrimination |