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Data mining techniques for transformer failure prediction model: A systematic literature review

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dc.citedby3
dc.contributor.authorRavi N.N.en_US
dc.contributor.authorMohd Drus S.en_US
dc.contributor.authorKrishnan P.S.en_US
dc.contributor.authorid57205240347en_US
dc.contributor.authorid56330463900en_US
dc.contributor.authorid36053261400en_US
dc.date.accessioned2023-05-29T07:26:05Z
dc.date.available2023-05-29T07:26:05Z
dc.date.issued2019
dc.descriptionClassification (of information); Forecasting; Industrial electronics; Outages; Power transformers; Preventive maintenance; Classification algorithm; Data mining algorithm; Maintenance strategies; Performance measurements; Prediction techniques; Predictive maintenance; Systematic literature review; Transformer failure; Data miningen_US
dc.description.abstractTransformer failure may occur in terms of tripping, resulting in an unplanned or unseen failure. Therefore, a good maintenance strategy is an essential component of a power system to prevent unanticipated failures. Routine preventive maintenance programs have traditionally been used in combination with regular tests. However, in recent years, predictive maintenance has become prevalent due to the demanding industrial needs. Due to the increased requirement, utilities are persistently looking for ways to overcome the challenge of power transformer failures. One of the most popular ways for fault prediction is data mining. Data mining techniques can be applied in transformer failure prediction to provide the possibility of failure occurrence. Thus, this study aims to identify the common data mining techniques and algorithms that are implemented in studies related to various transformer failure types. The accuracy of each algorithm is also studied in this paper. A systematic literature review is carried out by identifying 160 articles from four main databases of which 6 articles are chosen in the end. This review found that the most common prediction technique used is classification. Among the classification algorithms, ANN is the prominent algorithm adopted by most of the researchers which has provided the highest accuracy compared to other algorithms. Further research can be done to investigate more on the transformer failures types and fair comparison between multiple algorithms in order to get more precise performance measurement. � 2019 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8743987
dc.identifier.doi10.1109/ISCAIE.2019.8743987
dc.identifier.epage309
dc.identifier.scopus2-s2.0-85069153763
dc.identifier.spage305
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85069153763&doi=10.1109%2fISCAIE.2019.8743987&partnerID=40&md5=14e25b23e9cdf9b09d69b7e324ed4d3e
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24706
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleISCAIE 2019 - 2019 IEEE Symposium on Computer Applications and Industrial Electronics
dc.titleData mining techniques for transformer failure prediction model: A systematic literature reviewen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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