Substation transformer failure analysis through text mining
Data 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 Fa...
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Institute of Electrical and Electronics Engineers Inc.
2023
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my.uniten.dspace-247072023-05-29T15:26:06Z Substation transformer failure analysis through text mining Ravi N.N. Mohd Drus S. Krishnan P.S. Laila Abdul Ghani N. 57205240347 56330463900 36053261400 57215343667 Data 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) Transformer 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. Final 2023-05-29T07:26:06Z 2023-05-29T07:26:06Z 2019 Conference Paper 10.1109/ISCAIE.2019.8743719 2-s2.0-85069147125 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069147125&doi=10.1109%2fISCAIE.2019.8743719&partnerID=40&md5=02fb90e124930b3d1953c3e1ee009710 https://irepository.uniten.edu.my/handle/123456789/24707 8743719 293 298 Institute of Electrical and Electronics Engineers Inc. Scopus |
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| description |
Data 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) |
| author2 |
57205240347 |
| author_facet |
57205240347 Ravi N.N. Mohd Drus S. Krishnan P.S. Laila Abdul Ghani N. |
| format |
Conference Paper |
| author |
Ravi N.N. Mohd Drus S. Krishnan P.S. Laila Abdul Ghani N. |
| spellingShingle |
Ravi N.N. Mohd Drus S. Krishnan P.S. Laila Abdul Ghani N. Substation transformer failure analysis through text mining |
| author_sort |
Ravi N.N. |
| title |
Substation transformer failure analysis through text mining |
| title_short |
Substation transformer failure analysis through text mining |
| title_full |
Substation transformer failure analysis through text mining |
| title_fullStr |
Substation transformer failure analysis through text mining |
| title_full_unstemmed |
Substation transformer failure analysis through text mining |
| title_sort |
substation transformer failure analysis through text mining |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2023 |
| _version_ |
1806424498453348352 |
| score |
13.222552 |