Advanced SearchSearch Tips
Dissolved Gas Analysis of Power Transformer Using Fuzzy Clustering and Radial Basis Function Neural Network
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Dissolved Gas Analysis of Power Transformer Using Fuzzy Clustering and Radial Basis Function Neural Network
Lee, J.P.; Lee, D.J.; Kim, S.S.; Ji, P.S.; Lim, J.Y.;
  PDF(new window)
Diagnosis techniques based on the dissolved gas analysis(DGA) have been developed to detect incipient faults in power transformers. Various methods exist based on DGA such as IEC, Roger, Dornenburg, and etc. However, these methods have been applied to different problems with different standards. Furthermore, it is difficult to achieve an accurate diagnosis by DGA without experienced experts. In order to resolve these drawbacks, this paper proposes a novel diagnosis method using fuzzy clustering and a radial basis neural network(RBFNN). In the neural network, fuzzy clustering is effective for selecting the efficient training data and reducing learning process time. After fuzzy clustering, the RBF neural network is developed to analyze and diagnose the state of the transformer. The proposed method measures the possibility and degree of aging as well as the faults occurred in the transformer. To demonstrate the validity of the proposed method, various experiments are performed and their results are presented.
DGA;Diagnosis;Fuzzy Clustering;Power Transformer;RBF;
 Cited by
Integrated ANN-based proactive fault diagnostic scheme for power transformers using dissolved gas analysis, IEEE Transactions on Dielectrics and Electrical Insulation, 2016, 23, 3, 1838  crossref(new windwow)
The behavior of different transformer oils relating to the generation of fault gases after electrical flashovers, International Journal of Electrical Power & Energy Systems, 2017, 84, 261  crossref(new windwow)
H. Tsukioka, K. Sugawara, E. Mori and H. Yamaguchi, 'New apparatus for detecting transformer faults', IEEE Trans. Electrical Insulation, vol. EI-21, no. 2, pp. 221-229, 1986 crossref(new window)

M. Duval, 'Dissolved gas analysis: It can save your transformer', IEEE Electrical Insulation Magazine, vol. 5, no. 6, pp. 22-26, 1989

H. Yoshida, Y. Ishioka, T. Suzuki, T. Yanari and T. Teranishi, 'Degradation of insulating materials of transformers', IEEE Trans. Electrical Insulation, vol. EI-22, No. 6, pp. 795-800, 1987 crossref(new window)

Y. Kamata, 'Diagnostic methods for power transformer insulation', IEEE Trans. Electrical Insulation, vol. EI-21, no. 6, pp. 1045-1048, 1986 crossref(new window)

Fu Yang, Jin Xi; Lan Zhida, 'A neural network approach to power transformer fault diagnosis', ICEMS 2003, Electrical Machines and Systems, vol. 1, pp. 351–354, Nov. 2003

Pyeong Shik Ji, Jae Yoon Lim; Jong Pil Lee, 'Aging characteristics of power transformer oil and development of its analysis using KSOM', TENCON 99, Proceedings of the IEEE Region, vol. 2, pp. 1026- 029, Sept. 1999

Magn_Hui Wang, Hong-Chan Chang, 'Novel clustering method for coherency identification using an artificial neural network', IEEE Trans., Power Systems, vol. 9, pp. 2056-2062, Nov. 1994 crossref(new window)

J.L.Naredo, P. Moreno, C.R. Fuerte, 'A comparative study of neural network efficiency in power transformer diagnosis using dissolved gas analysis', IEEE Trans. Power Delivery, vol. 16, pp. 643 - 647, Oct. 2001 crossref(new window)

V. Miranda, A.R.G. Castro, 'Improving the IEC table for transformer failure diagnosis with knowledge extraction from neural networks', IEEE Trans. Power Delivery, vol. 20, pp. 2509-2516, Oct. 2005 crossref(new window)

Yann-Chang Huang, 'A new data mining approach to dissolved gas analysis of oil-insulated power apparatus', IEEE Trans. Power Delivery, vol. 18, pp. 1257-1261, Oct. 2003 crossref(new window)

Hong -Tzer Yang; Chiung-Chou Liao, 'Adaptive fuzzy diagnosis system for dissolved gas analysis of power transformers', IEEE Trans. Power Delivery, vol. 14, pp. 1342-1350, Oct. 1999 crossref(new window)

Mehmet K. Muezzinoglu, Jacek M. Zurada, 'RBFbased neurodynamic nearest neighbor classification in real pattern space', ARTICLE Pattern Recognition, In Press, Corrected Proof, Available online December 20, 2005

Jamuna Kanta Sing, Dipak Kumar Basu, Mita Nasipuri, Mahantapas Kundu, 'Face recognition using point symmetry distance-based RBF network,' ARTICLE Applied Soft Computing, In Press, Corrected Proof, Available online April 22, 2005

Sang Wook Choi, Dongkwon Lee, Jin Hyun Park, In- Beum Lee, 'Nonlinear regression using RBFN with linear submodels', Chemometrics and Intelligent Laboratory Systems, vol. 65, pp. 191-208, Feb. 2003 crossref(new window)

J.C. Bezedec, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981