Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/218247
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dc.contributor.authorJingmin Fanen_US
dc.contributor.authorFeng Wangen_US
dc.contributor.authorQiuqin Sunen_US
dc.contributor.authorFeng Binen_US
dc.contributor.authorFangwei Liangen_US
dc.contributor.authorXuanyi Xiaoen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T08:11:33Z-
dc.date.available2020-04-06T08:11:33Z-
dc.date.issued2017en_US
dc.identifier.other10.1049/iet-gtd.2017.0547en_US
dc.identifier.urihttp://localhost/handle/Hannan/218247-
dc.description.abstractDissolved gas analysis (DGA) is a popular method for diagnosing faults inside power transformers. However, some of the recorded data for the analysis are with ambiguous characteristic, leading to misdiagnosis of conventional methods. In this work, a hybrid method, which combines the relevance vector machine (RVM) and the adaptive neural fuzzy inference system (ANFIS) has been proposed to address this issue. Given the fuzziness between DGA records and fault type, and to minimise the number of rules that ANFIS needs to extract, the RVM algorithm performs binary separation firstly, and then ANFIS is utilised to achieve further fault diagnosis in this study. The experimental results demonstrate that the hybrid RVM-ANFIS algorithm can achieve an accuracy rate as high as 95%. Moreover, the proposed algorithm exceeds single ANFIS, support vector machine, and artificial neural network on distinguishing multiple faults and samples with ambiguous characteristic. The engineering application results also demonstrate the effectiveness and superiority of the proposed RVM-ANFIS.en_US
dc.format.extent3637,en_US
dc.format.extent3643en_US
dc.publisherIETen_US
dc.relation.haspart8068833.pdfen_US
dc.titleHybrid RVM–ANFIS algorithm for transformer fault diagnosisen_US
dc.typeArticleen_US
dc.journal.volume11en_US
dc.journal.issue14en_US
Appears in Collections:2017

Files in This Item:
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8068833.pdf1.43 MBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributor.authorJingmin Fanen_US
dc.contributor.authorFeng Wangen_US
dc.contributor.authorQiuqin Sunen_US
dc.contributor.authorFeng Binen_US
dc.contributor.authorFangwei Liangen_US
dc.contributor.authorXuanyi Xiaoen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T08:11:33Z-
dc.date.available2020-04-06T08:11:33Z-
dc.date.issued2017en_US
dc.identifier.other10.1049/iet-gtd.2017.0547en_US
dc.identifier.urihttp://localhost/handle/Hannan/218247-
dc.description.abstractDissolved gas analysis (DGA) is a popular method for diagnosing faults inside power transformers. However, some of the recorded data for the analysis are with ambiguous characteristic, leading to misdiagnosis of conventional methods. In this work, a hybrid method, which combines the relevance vector machine (RVM) and the adaptive neural fuzzy inference system (ANFIS) has been proposed to address this issue. Given the fuzziness between DGA records and fault type, and to minimise the number of rules that ANFIS needs to extract, the RVM algorithm performs binary separation firstly, and then ANFIS is utilised to achieve further fault diagnosis in this study. The experimental results demonstrate that the hybrid RVM-ANFIS algorithm can achieve an accuracy rate as high as 95%. Moreover, the proposed algorithm exceeds single ANFIS, support vector machine, and artificial neural network on distinguishing multiple faults and samples with ambiguous characteristic. The engineering application results also demonstrate the effectiveness and superiority of the proposed RVM-ANFIS.en_US
dc.format.extent3637,en_US
dc.format.extent3643en_US
dc.publisherIETen_US
dc.relation.haspart8068833.pdfen_US
dc.titleHybrid RVM–ANFIS algorithm for transformer fault diagnosisen_US
dc.typeArticleen_US
dc.journal.volume11en_US
dc.journal.issue14en_US
Appears in Collections:2017

Files in This Item:
File SizeFormat 
8068833.pdf1.43 MBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributor.authorJingmin Fanen_US
dc.contributor.authorFeng Wangen_US
dc.contributor.authorQiuqin Sunen_US
dc.contributor.authorFeng Binen_US
dc.contributor.authorFangwei Liangen_US
dc.contributor.authorXuanyi Xiaoen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T08:11:33Z-
dc.date.available2020-04-06T08:11:33Z-
dc.date.issued2017en_US
dc.identifier.other10.1049/iet-gtd.2017.0547en_US
dc.identifier.urihttp://localhost/handle/Hannan/218247-
dc.description.abstractDissolved gas analysis (DGA) is a popular method for diagnosing faults inside power transformers. However, some of the recorded data for the analysis are with ambiguous characteristic, leading to misdiagnosis of conventional methods. In this work, a hybrid method, which combines the relevance vector machine (RVM) and the adaptive neural fuzzy inference system (ANFIS) has been proposed to address this issue. Given the fuzziness between DGA records and fault type, and to minimise the number of rules that ANFIS needs to extract, the RVM algorithm performs binary separation firstly, and then ANFIS is utilised to achieve further fault diagnosis in this study. The experimental results demonstrate that the hybrid RVM-ANFIS algorithm can achieve an accuracy rate as high as 95%. Moreover, the proposed algorithm exceeds single ANFIS, support vector machine, and artificial neural network on distinguishing multiple faults and samples with ambiguous characteristic. The engineering application results also demonstrate the effectiveness and superiority of the proposed RVM-ANFIS.en_US
dc.format.extent3637,en_US
dc.format.extent3643en_US
dc.publisherIETen_US
dc.relation.haspart8068833.pdfen_US
dc.titleHybrid RVM–ANFIS algorithm for transformer fault diagnosisen_US
dc.typeArticleen_US
dc.journal.volume11en_US
dc.journal.issue14en_US
Appears in Collections:2017

Files in This Item:
File SizeFormat 
8068833.pdf1.43 MBAdobe PDF