Detecting Incipient Faults in Transformers: A dual Cascade Decision Tree Approach Using DGA
محورهای موضوعی : مهندسی هوشمند برق
Milad Shafiei asl
1
,
S.Benyamin Babaie
2
1 - Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
2 - Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
کلید واژه: power transformer, dissolved gas analysis, decision tree,
چکیده مقاله :
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.
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.
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