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Substation transformer failure analysis through text mining

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...

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Main Authors:	Ravi, N.N., Mohd Drus, S., Krishnan, P.S., Laila Abdul Ghani, N.
Format:	Conference Proceeding
Language:	English
Published:	2020
Subjects:	Power Transformer Text Mining Failure Analysis
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id	my.uniten.dspace-13093
record_format	dspace
spelling	my.uniten.dspace-130932020-08-19T00:23:52Z Substation transformer failure analysis through text mining Ravi, N.N. Mohd Drus, S. Krishnan, P.S. Laila Abdul Ghani, N. Power Transformer Text Mining Failure Analysis 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. 2020-02-03T03:30:20Z 2020-02-03T03:30:20Z 2019-06 Conference Proceeding 10.1109/ISCAIE.2019.8743719 en 2019 Ieee 9Th Symposium on Computer Applications & Industrial Electronics (Iscaie)
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
language	English
topic	Power Transformer Text Mining Failure Analysis
spellingShingle	Power Transformer Text Mining Failure Analysis Ravi, N.N. Mohd Drus, S. Krishnan, P.S. Laila Abdul Ghani, N. Substation transformer failure analysis through text mining
description	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.
format	Conference Proceeding
author	Ravi, N.N. Mohd Drus, S. Krishnan, P.S. Laila Abdul Ghani, N.
author_facet	Ravi, N.N. Mohd Drus, S. Krishnan, P.S. Laila Abdul Ghani, N.
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
publishDate	2020
_version_	1678595891790872576
score	13.222552

Substation transformer failure analysis through text mining

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