Comparative analysis of transformer fault classification based on DGA data using machine learning algorithms

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dc.authorid0000-0003-2181-070X
dc.authorid0000-0001-9528-7187
dc.authorid0000-0001-7664-103X
dc.contributor.authorÇoban, Melih
dc.contributor.authorFidan, Murat
dc.contributor.authorAytar, Oktay
dc.date.accessioned2024-09-25T20:00:08Z
dc.date.available2024-09-25T20:00:08Z
dc.date.issued2024
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü en_US
dc.description6th Global Power, Energy and Communication Conference (GPECOM) -- JUN 04-07, 2024 - Budapest, HUNGARYen_US
dc.description.abstractDissolved gas analysis (DGA) is considered a leading technique for fault classification in power transformers. However, accurate analysis results can only be achieved if the measured gases are interpreted, appropriately. In DGA interpretation, traditional techniques, artificial intelligence techniques such as machine learning algorithms, and hybrid techniques are generally used. In this study, four well-known machine learning algorithms have been compared in terms of DGA fault classification: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naive Bayes (NB) and Decision Tree (DT). The lowest accuracy rate was obtained as 63.63% using the NB algorithm and raw data. In addition to raw data, data converted to logarithmic form has been also used to develop classification models. The highest accuracy rate was determined as 94.54% using the DT algorithm and logarithmic data. The obtained results have been demonstrated the efficiency and stability of the DT algorithm for transformer fault classification, especially when the data was appropriately preprocessed.en_US
dc.description.sponsorshipIEEEen_US
dc.identifier.doi10.1109/GPECOM61896.2024.10582728
dc.identifier.endpage267en_US
dc.identifier.isbn979-8-3503-5108-8
dc.identifier.isbn979-8-3503-5109-5
dc.identifier.issn2832-7667
dc.identifier.scopus2-s2.0-85199051014en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage263en_US
dc.identifier.urihttps://doi.org/10.1109/GPECOM61896.2024.10582728
dc.identifier.urihttps://hdl.handle.net/20.500.12491/14089
dc.identifier.wosWOS:001268516300115en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorFidan, Murat
dc.institutionauthorÇoban, Melih
dc.institutionauthorAytar, Oktay
dc.institutionauthorid0000-0003-2181-070X
dc.institutionauthorid0000-0001-9528-7187
dc.institutionauthorid0000-0001-7664-103X
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartofProceedings 2024 Ieee 6th Global Power, Energy And Communication Conference, Ieee Gpecom 2024en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzYK_20240925en_US
dc.subjectTransformer Fault Classificationen_US
dc.subjectMachine Learning Algorithmsen_US
dc.subjectDissolved Gas Analysisen_US
dc.subjectTraditional Techniques
dc.subjectArtificial Intelligence
dc.subjectDT Algorithm
dc.titleComparative analysis of transformer fault classification based on DGA data using machine learning algorithmsen_US
dc.typeConference Objecten_US

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