Diagnosing failed distribution transformers using neural networks
An artificial neural networks (ANN) system was developed for distribution transformer's failure diagnosis. The diagnosis was based on the latest standards and expert experiences in this field. The ANN was trained utilizing backpropagation algorithm using a real (out of the field) data obtained...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , |
| التنسيق: | article |
| منشور في: |
2001
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://eprints.kfupm.edu.sa/id/eprint/14445/1/14445_1.pdf https://eprints.kfupm.edu.sa/id/eprint/14445/2/14445_2.doc |
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| _version_ | 1864513384115863552 |
|---|---|
| author | Farag, A.S. |
| author2 | Mohandes, M. Al-Shaikh, A. unknown |
| author2_role | author author author |
| author_facet | Farag, A.S. Mohandes, M. Al-Shaikh, A. unknown |
| author_role | author |
| dc.creator.none.fl_str_mv | Farag, A.S. Mohandes, M. Al-Shaikh, A. unknown |
| dc.date.none.fl_str_mv | 2001-10 2020 |
| dc.format.none.fl_str_mv | application/pdf application/msword |
| dc.identifier.none.fl_str_mv | https://eprints.kfupm.edu.sa/id/eprint/14445/1/14445_1.pdf https://eprints.kfupm.edu.sa/id/eprint/14445/2/14445_2.doc (2001) Diagnosing failed distribution transformers using neural networks. Power Delivery, IEEE Transactions on, 16. |
| dc.language.none.fl_str_mv | en en |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.relation.none.fl_str_mv | https://eprints.kfupm.edu.sa/id/eprint/14445/ |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Computer |
| dc.title.none.fl_str_mv | Diagnosing failed distribution transformers using neural networks |
| dc.type.none.fl_str_mv | Article PeerReviewed info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | An artificial neural networks (ANN) system was developed for distribution transformer's failure diagnosis. The diagnosis was based on the latest standards and expert experiences in this field. The ANN was trained utilizing backpropagation algorithm using a real (out of the field) data obtained from utilities distribution networks transformer's failures. The ANN consists of six individual ANN according to six important factors used to give certain outputs. These factors are: the age of the transformer, the weather condition, if there are any damaged bushings, if there are any damaged casing or enclosure, if there is oil leakage, and if there are any faults in the windings. The six ANNs are combined in one ANN to give all the outputs of the individual six ANNs. The developed ANN can be used to give recommended complete diagnosis for working transformers to avoid possible failures depending on their operating conditions. Good diagnosis accuracy is obtained with the proposed approach applied and with the analysis of the attainable results |
| eu_rights_str_mv | openAccess |
| format | article |
| id | KFUPM_2e92a5a5eae09c17c0b9d9e72c120dce |
| identifier_str_mv | (2001) Diagnosing failed distribution transformers using neural networks. Power Delivery, IEEE Transactions on, 16. |
| language_invalid_str_mv | en |
| network_acronym_str | KFUPM |
| network_name_str | King Fahd University of Petroleum and Minerals |
| oai_identifier_str | oai::14445 |
| publishDate | 2001 |
| publisher.none.fl_str_mv | IEEE |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Diagnosing failed distribution transformers using neural networksFarag, A.S.Mohandes, M.Al-Shaikh, A.unknownComputerAn artificial neural networks (ANN) system was developed for distribution transformer's failure diagnosis. The diagnosis was based on the latest standards and expert experiences in this field. The ANN was trained utilizing backpropagation algorithm using a real (out of the field) data obtained from utilities distribution networks transformer's failures. The ANN consists of six individual ANN according to six important factors used to give certain outputs. These factors are: the age of the transformer, the weather condition, if there are any damaged bushings, if there are any damaged casing or enclosure, if there is oil leakage, and if there are any faults in the windings. The six ANNs are combined in one ANN to give all the outputs of the individual six ANNs. The developed ANN can be used to give recommended complete diagnosis for working transformers to avoid possible failures depending on their operating conditions. Good diagnosis accuracy is obtained with the proposed approach applied and with the analysis of the attainable resultsIEEE2001-102020ArticlePeerReviewedinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfapplication/mswordhttps://eprints.kfupm.edu.sa/id/eprint/14445/1/14445_1.pdfhttps://eprints.kfupm.edu.sa/id/eprint/14445/2/14445_2.doc (2001) Diagnosing failed distribution transformers using neural networks. Power Delivery, IEEE Transactions on, 16. enenhttps://eprints.kfupm.edu.sa/id/eprint/14445/info:eu-repo/semantics/openAccessoai::144452019-11-01T14:05:51Z |
| spellingShingle | Diagnosing failed distribution transformers using neural networks Farag, A.S. Computer |
| status_str | publishedVersion |
| title | Diagnosing failed distribution transformers using neural networks |
| title_full | Diagnosing failed distribution transformers using neural networks |
| title_fullStr | Diagnosing failed distribution transformers using neural networks |
| title_full_unstemmed | Diagnosing failed distribution transformers using neural networks |
| title_short | Diagnosing failed distribution transformers using neural networks |
| title_sort | Diagnosing failed distribution transformers using neural networks |
| topic | Computer |
| url | https://eprints.kfupm.edu.sa/id/eprint/14445/1/14445_1.pdf https://eprints.kfupm.edu.sa/id/eprint/14445/2/14445_2.doc |