Power Transformer Fault Diagnosis based on Dissolved Gas Analysis with Logistic Regression

• Autorzy: Božić M. , Stojanović M. , Stajić Z. , Vukić D.

Warianty tytułu

PL

Diagnostyki transformatora bazująca na analizie rozpuszczonego gazu metodą logistycznej regresji

• Języki publikacji: EN

Abstrakty

EN

Logistic regression (LR) approach for power transformer fault diagnosis, based on dissolved gas analysis (DGA) is presented in this paper. DGA methods proposed by actual standard IEC 60599, often identify wrong fault or cannot even recognize fault type. To overcome these problems, in recent years, several artificial intelligence (AI) approaches are proposed. In this paper LR is applied for the first time in multi-layer and multi class configuration models for transformer fault diagnosis. It is shown that the proposed approach gives a very good classification performance.

PL

Przedstawiono metodę diagnostyki transformatora bazująca na analizie rozpuszczonego gazu metodą logistycznej regresji. Metoda ta umożliwia nie tylko wykrywanie uszkodzeń ale także ich klasyfikację.

Słowa kluczowe

EN

power transformer; dissolved gas analysis; logistic regression; fault diagnosis

PL

diagnostyka transformatora; analiza gazu; logistyczna regresja

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2013
• Tom: R. 89, nr 6
• Strony: 83--87
• Opis fizyczny: Bibliogr. 20 poz., rys., tab.

Twórcy

autor

Božić M.

• Faculty of Electronic Engineering, University of Niš

autor

Stojanović M.

• School of higher Technical Professional Education

autor

Stajić Z.

• Faculty of Electronic Engineering, University of Niš

autor

Vukić D.

• Faculty of Agriculture, University of Belgrade

Bibliografia

• [1] ABB, Service Handbook for Transformers - ABB Zurich, Switzerland, 2006.
• [2] United States Department of the Interior (Bureau of Reclamation), Transformer Maintenance Manual, Facilitates Instructions, Standards and Techniques, 3 (2000), No. 33.
• [3] IEEE Standard C57.104-1991 - Guide for the Interpretation of Gasses Generated in Oil-Immersed Transformers, (1991).
• [4] R.R. Rogers, IEEE and IEC Codes to Interpret Incipient Faults in Transformers Using Gas in Oil Analysis, CEGB Transmission division, (1995).
• [5] International Electro Technical Commission (IEC 60599), Mineral Oil-Impregnated Electrical Equipment in Service, Interpretation of Dissolved and Free Gas Analysis - IEC, (2007).
• [6] Duval M., A review of faults detectable by gas-in-oil analysis in transformers, IEEE Electrical Insulation Magazine, 18 (2002), 8-17.
• [7] Q. Su, C. Mi, L.L. Lai, P. Austin, A fuzzy dissolved gas analysis method for the diagnosis of multiple incipient faults in a transformer, IEEE Trans. Power Syst., 15 (2000), No. 2, 593- 598.
• [8] C.E. Lin, J.M. Ling, C.L. Huang, An expert system for transformer fault diagnosis using dissolved gas analysis, IEEE Trans. Power Del., 8 (1993), No. 1, 231-238.
• [9] Y. Zhang, X. Ding, Y. Liu, P.J. Griffin, An artificial neural network approach to transformer fault diagnosis, IEEE Trans. Power Del., 11 (1996), No. 4, 1836-1841.
• [10] K. Thang, R. Aggarwal, A. McGrail, D. Esp, Analysis of power transformer dissolved gas data using the self-organizing map, IEEE Trans. Power Del., 18 (2003), No. 4, 1241-1248.
• [11] V.N. Vapnik, The Nature of Statistical Learning Theory - Springer-Veriag, New York, (1995).
• [12] Piotr S. SZCZEPANIAK, Marcin KŁOSIŃSKI, Maximal margin classifiers applied to DGA-based diagnosis of power transformers, PRZEGLĄD ELEKTROTECHNICZNY (Electrical Review), 2 (2012), 100-104.
• [13] Mu ZHANG, Kun LI, Huixin TIAN, Multiple SVMs Modeling Method for Fault Diagnosis of Power Transformers, PRZEGLĄD ELEKTROTECHNICZNY (Electrical Review), 7b (2012), 232-234.
• [14] L. Ganyun, H. Cheng, H. Zhai, L. Dong, Fault diagnosis of power transformer based on multi-layer SVM classifier, Electric Power Systems Research, 75 (2005), 9-15.
• [15] S.-w. Fei, X.-b. Zhang, Fault diagnosis of power transformer based on support vector machine with genetic algorithm, Expert Systems with Applications, 36 (2009), 11352-11357.
• [16] K. Bacha, S. Souahlia, M. Gossa, Power transformer diagnosis based on dissolved gas analysis by support vector machines, Electric Power System Research, 83 (2012), 73-79.
• [17] D. Hosmer, S. Lemeshow, Applied Logistic Regression (2nd ed.) - John Wiley & Sons, (2000).
• [18] C. Bielza, V. Robles, P. Larrañaga, Regularized logistic regression without a penalty term: An application to cancer classification with microarray data, Expert Systems with Applications, 38 (2011), 5110–5118.
• [19] R. Rifkin, A. Klautau, In Defense of One-Vs-All Classification, Journal of Machine Learning Research, 5 (2004), 101-141.
• [20] R. E. Fan, C. J. Lin, A study on threshold selection for multilabel classification, Technical Report, National Taiwan University, (2007).