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Substation transformer failure analysis through text mining

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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.authorLaila Abdul Ghani N.en_US
dc.contributor.authorid57205240347en_US
dc.contributor.authorid56330463900en_US
dc.contributor.authorid36053261400en_US
dc.contributor.authorid57215343667en_US
dc.date.accessioned2023-05-29T07:26:06Z
dc.date.available2023-05-29T07:26:06Z
dc.date.issued2019
dc.descriptionData mining; Data visualization; Failure analysis; Industrial electronics; Linguistics; Outages; Power transformers; Predictive analytics; Transformer substations; Maintenance strategies; Problem description; R languages; Substation transformers; Text mining; Transformer systems; Trip; Unexpected Failures; Failure (mechanical)en_US
dc.description.abstractTransformer failure could occur in terms of tripping that results in an unplanned or unseen outage. A good maintenance strategy is therefore an essential component in a power system to prevent unexpected failures. In this paper, the causes of transformer failure within the power transformer systems have been reviewed. Data is obtained from the transmission substation assets from the whole of Peninsular Malaysia for the past 5 years. However, the challenge is that the problem descriptions of the datasets are all in text formats. Thus, text mining approach is chosen for the data analysis using R. This paper covers the most common steps in R, from data preparation to analysis, and visualization through wordcloud generation. This study mainly focuses on bag-of-word text analysis approaches, which means that only word frequencies per text are used and word positions are ignored. Although this simplifies text content dramatically, research and many applications in the real world show that word frequencies alone contain adequate information for many types of analysis. As a result of analysis, keywords like "leak", "lightning", "animal", "cable" and "temperature" are identified as the main causes of transformer failures based on the number of word frequency in the tripping dataset. Further enhancement could be made in the future to predict the failure beforehand using predictive analytics approaches. � 2019 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8743719
dc.identifier.doi10.1109/ISCAIE.2019.8743719
dc.identifier.epage298
dc.identifier.scopus2-s2.0-85069147125
dc.identifier.spage293
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85069147125&doi=10.1109%2fISCAIE.2019.8743719&partnerID=40&md5=02fb90e124930b3d1953c3e1ee009710
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24707
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.titleSubstation transformer failure analysis through text miningen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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