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

Subject Areas : International Journal of Smart Electrical Engineering

Milad Shafiei asl ¹ (Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran)
S.Benyamin Babaie ² (Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran)

Received: 2024-05-30 Accepted : 2024-06-17 Published : 2024-08-07

Keywords: power transformer, dissolved gas analysis, decision tree,

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.

References:

[1] A. G. C. Menezes, M. M. Araujo, O. M. Almeida, F. R. Barbosa, and A. P. S. Braga, "Induction of decision trees to diagnose incipient faults in power transformers," IEEE Transactions on dielectrics and electrical insulation, vol. 29, no. 1, pp. 279-286, 2022.

[2] J. Faiz and M. Soleimani, "Dissolved gas analysis evaluation in electric power transformers using conventional methods a review," IEEE Transactions on Dielectrics and Electrical Insulation, vol. 24, no. 2, pp. 1239-1248, 2017.

[3] IEEE Guide for the Interpretation of Gases Generated in Mineral Oil-Immersed Transformers, IEEE Std C57.104-2019, 2019.

[4] E. Dornenburg and W. Strittmatter, "Monitoring oil-cooled transformers by gas-analysis," Brown Boveri Review, vol. 61, no. 5, pp. 238-247, 1974.

[5] R. R. Rogers, "IEEE and IEC codes to interpret incipient faults in transformers, using gas in oil analysis," IEEE transactions on electrical insulation, no. 5, pp. 349-354, 1978.

[6] M. Duval, "A review of faults detectable by gas-in-oil analysis in transformers," IEEE electrical Insulation magazine, vol. 18, no. 3, pp. 8-17, 2002.

[7] M. Duval and L. Lamarre, "The duval pentagon-a new complementary tool for the interpretation of dissolved gas analysis in transformers," IEEE Electrical Insulation Magazine, vol. 30, no. 6, pp. 9-12, 2014.

[8] J. Faiz and M. Soleimani, "Assessment of computational intelligence and conventional dissolved gas analysis methods for transformer fault diagnosis," IEEE Transactions on Dielectrics and Electrical Insulation, vol. 25, no. 5, pp. 1798-1806, 2018.

[9] S. S. M. Ghoneim, I. B. M. Taha, and N. I. Elkalashy, "Integrated ANN-based proactive fault diagnostic scheme for power transformers using dissolved gas analysis," IEEE Transactions on Dielectrics and Electrical Insulation, vol. 23, no. 3, pp. 1838-1845, 2016.

[10] M. Noori, R. Effatnejad, and P. Hajihosseini, "Using dissolved gas analysis results to detect and isolate the internal faults of power transformers by applying a fuzzy logic method," IET Generation, Transmission & Distribution, vol. 11, no. 10, pp. 2721-2729, 2017.

[11] Y. Benmahamed, M. Teguar, and A. Boubakeur, "Application of SVM and KNN to Duval Pentagon 1 for transformer oil diagnosis," IEEE Transactions on Dielectrics and Electrical Insulation, vol. 24, no. 6, pp. 3443-3451, 2017.

[12] N. Haque, A. Jamshed, K. Chatterjee, and S. Chatterjee, "Accurate sensing of power transformer faults from dissolved gas data using random forest classifier aided by data clustering method," IEEE Sensors Journal, vol. 22, no. 6, pp. 5902-5910, 2022.

[13] T. Hastie, R. Tibshirani, J. H. Friedman, and J. H. Friedman, The elements of statistical learning: data mining, inference, and prediction. Springer, 2009.

[14] "Egyptian Electricity Holding Company (EEHC) Reports," 1991-2016.

[15] Mineral oil-filled electrical equipment in service - Guidance on the interpretation of dissolved and free gases analysis., IEC Std. 60599, 2022.