Detecting incipient faults in transformers: A dual cascade decision tree approach using DGA

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Shafiei asl Milad; Babaie Seyed Benyamin

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: International Journal of Smart Electrical Engineering Year:2024 | Volume:13 | Issue:2 Page(s): 117-123

Download Full-Text

View:

Download:

Cites:

Information Journal Paper

Title

Detecting incipient faults in transformers: A dual cascade decision tree approach using DGA

Author(s)

Shafiei asl Milad | Babaie Seyed Benyamin | Issue Writer Certificate

Keywords

power transformerQ4

dissolved gas analysisQ4

decision treeQ4

Abstract

The prompt diagnosis of abnormalities in power transformers is of paramount importance. dissolved gas analysis (DGA) serves as an essential and vital tool for identifying faults. This paper introduces a method based on a decision tree (DT) algorithm using DGA to assess the condition of transformer oil samples in two steps: Normal/Faulty and Fault Type. The DTs in this paper were trained using 80% of the 729-sample dataset and evaluated with the remaining 20%. The dataset includes concentrations of five gases dissolved in transformer mineral oil: H2, CH4, C2H2, C2H4, and C2H6. These key features, along with other necessary parameters for learning DTs, contribute to the analysis. By employing two separate and sequential DTs for diagnosing transformer oil samples, the proposed method significantly improves the accuracy of identifying the health status and the type of potential fault. In the test samples, the method achieved a precision of 95.5% for normal state detection and 78.3% for fault type identification.

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops