Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | ISCAIE 2019 - 2019 IEEE Symposium on Computer Applications and Industrial Electronics |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 293-298 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781538685464 |
| DOIs | |
| Publication status | Published - 01 Apr 2019 |
| Event | 9th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2019 - Kota Kinabalu, Malaysia Duration: 27 Apr 2019 → 28 Apr 2019 |
Publication series
| Name | ISCAIE 2019 - 2019 IEEE Symposium on Computer Applications and Industrial Electronics |
|---|
Conference
| Conference | 9th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2019 |
|---|---|
| Country | Malaysia |
| City | Kota Kinabalu |
| Period | 27/04/19 → 28/04/19 |
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All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- Artificial Intelligence
- Computer Science Applications
- Energy Engineering and Power Technology
- Industrial and Manufacturing Engineering
Cite this
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Substation transformer failure analysis through text mining. / Ravi, Nanthiine Nair; Mohd Drus, Sulfeeza; Krishnan, Prajindra Sankar; Laila Abdul Ghani, Nur.
ISCAIE 2019 - 2019 IEEE Symposium on Computer Applications and Industrial Electronics. Institute of Electrical and Electronics Engineers Inc., 2019. p. 293-298 8743719 (ISCAIE 2019 - 2019 IEEE Symposium on Computer Applications and Industrial Electronics).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Substation transformer failure analysis through text mining
AU - Ravi, Nanthiine Nair
AU - Mohd Drus, Sulfeeza
AU - Krishnan, Prajindra Sankar
AU - Laila Abdul Ghani, Nur
PY - 2019/4/1
Y1 - 2019/4/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85069147125&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85069147125&partnerID=8YFLogxK
U2 - 10.1109/ISCAIE.2019.8743719
DO - 10.1109/ISCAIE.2019.8743719
M3 - Conference contribution
AN - SCOPUS:85069147125
T3 - ISCAIE 2019 - 2019 IEEE Symposium on Computer Applications and Industrial Electronics
SP - 293
EP - 298
BT - ISCAIE 2019 - 2019 IEEE Symposium on Computer Applications and Industrial Electronics
PB - Institute of Electrical and Electronics Engineers Inc.
ER -