Login Register Cart 0
Generic placeholder image

Recent Advances in Computer Science and Communications

Editor-in-Chief

ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

Back
Journal Home About Journal Editorial Board Current Issue Volumes/Issues
Subscribe
Research Article

Power Transformer Fault Diagnosis using DGA and Artificial Intelligence

Author(s): Seyed Javad Tabatabaei Shahrabad, Vahid Ghods* and Mohammad Tolou Askari

Volume 13, Issue 4, 2020

Page: [579 - 587] Pages: 9

DOI: 10.2174/2213275912666190212124133

Price: $65

Abstract

Background: Power transformers are one of the most applicable electricity network devices which transmit output power of the generator to the network through increasing voltage and decreasing current. Due to high cost of such devices and cost of disconnecting device upon failure, disconnection and failure of the transformer should be avoided as much as possible.

Objective: In addition, in order to increase reliability and reduce maintenance costs, such devices should be monitored constantly. Internal faults ionize and warm up oil and as a result, gases like carbon dioxide, methane, ethane, ethylene and acetylene are produced. Various methods have been proposed for diagnosing fault in power transformers where one of the most well-known methods is dissolved gas analysis (DGA). DGA in oil is one of the effective tools for diagnosing initial faults in transformers.

Method: Common fault detection methods using oil-dissolved gas analysis include Dornemburge, Duval’s triangle, IEC/IEEE standard, key gases and Rogers. In recent years, artificial intelligence like genetic algorithm, fuzzy logic and neural networks have been used to detect faults using DGA. In this paper, support vector machine (SVM) and decision tree are used to detect internal faults in power transformers.

Results: By evaluation of the proposed methods, total accuracies of classifiers using SVM and decision tree were 90% and 97.5%, respectively.

Conclusion: Decision tree shows better performance and it is suggested as a proper method for obtaining promising results.

Keywords: Power transformer, oil immersed transformer, DGA, fault diagnosis, artificial intelligence, support vector machine (SVM)

Graphical Abstract

[1]
J.J. Kelly . “Transformer fault diagnosis by dissolved-gas analysis” . IEEE Transac Indus Appl Vol. 16, No. 6, pp. 777-782, 1980
[http://dx.doi.org/10.1109/TIA.1980.4503871]
[2]
“Transformers Committee IEEE guide for the interpretation of gases generated in oil-immersed transformers Institute of Electrical & Electronics Engineers”, Inc New York. 1992.
[http://dx.doi.org/10.1109/IEEESTD.1992.106973]
[3]
Z. Weizheng , W. Zhenggang , F. Yingshuan , and L. Fazhan . “The application of compound neural network in condition estimate of power transformer”. WSEAS Transac Circ Syst Vol. 7, No. 12, pp. 1029-1038, 2008.
[4]
M.A.B. Amora , O.M. Almeida , A.P.S. Braga , F.R. Barbosa , L.A.C. Lisboa , and R.S.T. Pontes . “Improved DGA method based on rules extracted from high-dimension input space”. Electron Lett Vol. 48, No. 17, pp. 1048-1049, 2012.
[http://dx.doi.org/10.1049/el.2012.1363]
[5]
W.S. Lin , C.P. Hung , and M.H. Wang . “CMAC-based fault diagnosis of power transformers, Neural Networks” Proceedings of the 2002 International Joint Conference on Honolulu HI,. 2002 pp 986-991.
[http://dx.doi.org/10.1109/IJCNN.2002.1005609]
[6]
S.A. Khan , M.D. Equbal , and T. Islam . “A comprehensive comparative study of DGA based transformer fault diagnosis using fuzzy logic”. IEEE Transac Dielectric Electric Insul Vol. 2, No. 1, pp. 590-596, 2015.
[7]
S.W. Fei , and Y. He . “A multi-layer KMC-RS-SVM classifier and DGA for fault diagnosis of power transformer”. Rec Pat Computer Sci Vol. 5, No. 3, pp. 238-243, 2012.
[8]
Y. Liang , Y.Y. Xu , X.S. Wan , Y. Li , N. Liu , and G.J. Zhang . “Dissolved gas analysis of transformer oil based on deep belief networks” 12th International Conference on the Properties and Applications of Dielectric Materials (ICPADM) China 2018, pp. 825-828.
[9]
J.I. Aizpurua , V.M. Catterson , B.G. Stewart , et al. “Power transformer dissolved gas analysis through Bayesian networks and hypothesis testing”. IEEE Transac Dielectric Electric Insul Vol. 25, No. 2, pp. 494-506, 2018.
[10]
Z. Ayalew , K. Kobayashi , S. Matsumoto , and M. Kato . “Dissolved Gas Analysis (DGA) of arc discharge fault in transformer insulation oils (ester and mineral oils)”. In: 2018 IEEE Electrical Insulation Conference (EIC) 2018; 150-3.
[11]
I.B. Taha , D.E.A. Mansour , S.S. Ghoneim , and N.I. Elkalashy . “Conditional probability-based interpretation of dissolved gas analysis for transformer incipient faults”. IET Generat Transmis & Distrib Vol. 11, No. 4, pp. 943-951, 2017.
[12]
S.S. Ghoneim , I.B. Taha , and N.I. Elkalashy . “Integrated ANN-based proactive fault diagnostic scheme for power transformers using dissolved gas analysis”. IEEE Transac Dielectric Electric Insul Vol. 23, No. 3, pp. 1838-1845, 2016.
[13]
M. SuganyaBharathi, M.W. Iruthayarajan, S.S. Shunmugam, and L. Kalaivani, “Interpretation of dissolved gas analysis in transformer oil using fuzzy logic system.” In Power, Energy and Control (ICPEC), 2013.International Conference IEEE, 2013, pp. 245-249
[14]
H. Malik , and S. Mishra . “Application of Gene Expression Programming (GEP) in power transformers fault diagnosis using DGA”. IEEE Transac Indus Appl Vol. 52, No. 6, pp. 4556-4565, 2016.
[15]
T.S. Furey , N. Cristianini , N. Duffy , D.W. Bednarski , M. Schummer , and D. Haussler . “Support vector machine classification and validation of cancer tissue samples using microarray expression data”. Bioinformatics Vol. 16, No. 10, pp. 906-914, 2000.
[16]
H. Bhavsar , and M.H. Panchal . “A review on support vector machine for data classification”. Int J Advan Res Comp Eng Technol (IJARCET), Vol. 1, No. 10, pp. 185-189, 2012.

Rights & Permissions Print Cite
Article Metrics
22
© 2024 Bentham Science Publishers | Privacy Policy